{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Userdata\Shared\Logs\Ethnic Enclaves, political participation\log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 3 Jun 2020, 17:31:34
{txt}
{com}. 
. *log using "C:\Userdata\Shared\Logs\Ethnic Enclaves, political participation\log_familydata", replace
. 
. 
. 
. ///THE FOLLOWING LINE OF CODE PICKS OUT THOSE WITH HOUSEHOLD MEMEBERS IN THE COUNTRY 1987-1989
> ///THIS IS DONE SEPARATELY FROM 1990-1991, SINCE THE 
> ///HOUSEHOLD ID VARIABLE ONLY EXISTS FROM 1990
> 
. foreach y in 87 88 89 {c -(}
{txt}  2{com}. 
. *open data file with info on all migrations over a large period of time 
. use "RTB\migrationer"
{txt}  3{com}. 
. *Create immigration variable - keep immigrants - drop emmigrations
. 
. tab PostTyp, gen(m)
{txt}  4{com}.         keep if m1==1
{txt}  5{com}.         drop PostTyp m1 m2
{txt}  6{com}.  
. *Generate year and month of arrival - 
. 
. tostring Datum, replace
{txt}  7{com}. gen im_year = substr(Datum, 1, 4) 
{txt}  8{com}. destring im_year, replace
{txt}  9{com}. 
. gen im_month = substr(Datum, 5, 2) 
{txt} 10{com}. destring im_month, replace
{txt} 11{com}. 
. *keep only year in question 
. 
. keep if im_year==19`y'
{txt} 12{com}. 
. *small share who immigrated twise during a year - only need one obs. 
. 
. duplicates tag LopNr, gen(tag)
{txt} 13{com}. sort tag LopNr
{txt} 14{com}. 
. *Keep the observation from the latest immigration month
. bysort LopNr: egen latestmonth = max(im_month)
{txt} 15{com}.         drop if im_month!=latestmonth & tag>0
{txt} 16{com}. *check for duplicates - OK
.         duplicates report LopNr
{txt} 17{com}.         drop tag latestmonth
{txt} 18{com}. 
. ///Now merge on data from 1990 with information on households
> 
. merge 1:m LopNr using "LISA\Lisa_1990"
{txt} 19{com}. drop _merge
{txt} 20{com}. 
. *is there information about family? Otherwise drop 
. drop if FamId==.        
{txt} 21{com}. 
. *Number of household members
. duplicates tag FamId, gen(tag_household)
{txt} 22{com}. 
. *Singles - these did not have family - drop.
. *will be small measurement error here: 
. *an ind who arrvived as a family migrant 1988 (for example), and divorced in 1989
. *will be single in the data. We expect this to be few ind.  
. gen single = 1 if tag_household==0
{txt} 23{com}. drop if single==1
{txt} 24{com}. 
. *FAMILY with you - define as arriving in the same year as household member:
. duplicates tag FamId im_year, gen(tag_hh2)
{txt} 25{com}. gen family_im = 1 if im_year==19`y' & tag_household>0 & tag_household==tag_hh2
{txt} 26{com}. drop if family_im==1
{txt} 27{com}. 
. *Remaining immigrant arriving in `y' should be family migrants:
. * they did not arrive as singles (household member in the country)
. * and household member did not arrive same year
. 
. *However, Since we only have housholds in 1990
. *Need to drop the kins who arrived after 1987, 1988 and 1989
. *otherewise we are going to treat an individual who arrived in 1987
. *and had family coming in 1989, as a family migrant
. 
. *Take out a file with all immigrants who arrived 1988-1990
. preserve
{txt} 28{com}. duplicates drop LopNr, force
{txt} 29{com}. *keep only those that did not arrive in year`y'
. keep if im_year==.
{txt} 30{com}. rename Datum dtm
{txt} 31{com}. *merge with migration data and keep only migrants
. merge 1:m LopNr using "RTB\migrationer", keepusing(Datum PostTyp)
{txt} 32{com}. keep if _merge==3
{txt} 33{com}. drop _merge
{txt} 34{com}. *Only immigrants
. tab PostTyp, gen(m)
{txt} 35{com}.         keep if m1==1
{txt} 36{com}.         drop PostTyp m1 m2
{txt} 37{com}. tostring Datum, replace
{txt} 38{com}. *immigration year
. gen im_year2 = substr(Datum, 1, 4) 
{txt} 39{com}. destring im_year2, replace
{txt} 40{com}. *family arriving after 1990 not intersting - since houshold id based on 1990
. drop if im_year2>1990
{txt} 41{com}. gen spec = 1 if im_year2>19`y'
{txt} 42{com}. duplicates drop LopNr, force
{txt} 43{com}. 
. save "E:\ProjData\Ethnic Enclaves, political participation\temp\tempfamily", replace
{txt} 44{com}. restore 
{txt} 45{com}. 
. merge m:1 LopNr using "E:\ProjData\Ethnic Enclaves, political participation\temp\tempfamily"
{txt} 46{com}. 
. ///drop those who imigrated after 1987 and redo the household calculation
> drop if spec==1
{txt} 47{com}. 
. ///household member in the family, only prior year household members
> drop tag_household
{txt} 48{com}. duplicates tag FamId, gen(tag_household)
{txt} 49{com}. 
. *Family in the country
. gen anhorig = 1 if im_year==19`y' & tag_household>0 
{txt} 50{com}. 
. keep LopNr anhorig  
{txt} 51{com}. 
. *drop a very small number of duplicates
. duplicates drop LopNr, force
{txt} 52{com}. 
.         save "E:\ProjData\Ethnic Enclaves, political participation\temp\hushåll`y'", replace
{txt} 53{com}. {c )-}

{txt}Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,088,900 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(48 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}42721             0
{txt}{hline 10}{c BT}{hline 27}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,922,104
{txt}{col 9}from master{col 30}{res}          14,520{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,907,584{txt}  (_merge==2)

{col 5}matched{col 30}{res}          28,207{txt}  (_merge==3)
{col 5}{hline 41}
(14,520 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId{p_end}
{txt}(4,601,698 missing values generated)
(2,334,093 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId im_year{p_end}
{txt}(4,593,934 missing values generated)
(7,764 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
(8,502 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,183,519
{txt}{col 9}from master{col 30}{res}       4,034,910{txt}  (_merge==1)
{col 9}from using{col 30}{res}       4,148,609{txt}  (_merge==2)

{col 5}matched{col 30}{res}         852,533{txt}  (_merge==3)
{col 5}{hline 41}
(8,183,519 observations deleted)

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}    582,497       68.33       68.33
{txt}        Utv {c |}{res}    270,036       31.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    852,533      100.00
{txt}(270,036 observations deleted)
Datum was {res:long} now {res:str8}
im_year2: all characters numeric; {res}replaced {txt}as {res}int
{txt}(94,722 observations deleted)
(424,207 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(35,900 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\tempfamily.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,142,059
{txt}{col 9}from master{col 30}{res}       4,142,059{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}matched{col 30}{res}         451,875{txt}  (_merge==3)
{col 5}{hline 41}
(60,956 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId{p_end}
{txt}(4,526,180 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
file E:\ProjData\Ethnic Enclaves, political participation\temp\hushåll87.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,080,362 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(50 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}51257             0
{txt}{hline 10}{c BT}{hline 27}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,918,158
{txt}{col 9}from master{col 30}{res}          16,818{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,901,340{txt}  (_merge==2)

{col 5}matched{col 30}{res}          34,451{txt}  (_merge==3)
{col 5}{hline 41}
(16,818 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId{p_end}
{txt}(4,601,698 missing values generated)
(2,334,093 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId im_year{p_end}
{txt}(4,592,175 missing values generated)
(9,523 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
(10,033 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,189,313
{txt}{col 9}from master{col 30}{res}       4,034,910{txt}  (_merge==1)
{col 9}from using{col 30}{res}       4,154,403{txt}  (_merge==2)

{col 5}matched{col 30}{res}         846,739{txt}  (_merge==3)
{col 5}{hline 41}
(8,189,313 observations deleted)

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}    578,654       68.34       68.34
{txt}        Utv {c |}{res}    268,085       31.66      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    846,739      100.00
{txt}(268,085 observations deleted)
Datum was {res:long} now {res:str8}
im_year2: all characters numeric; {res}replaced {txt}as {res}int
{txt}(94,257 observations deleted)
(440,292 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(35,812 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\tempfamily.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,143,590
{txt}{col 9}from master{col 30}{res}       4,143,590{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}matched{col 30}{res}         448,585{txt}  (_merge==3)
{col 5}{hline 41}
(42,307 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId{p_end}
{txt}(4,541,597 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
file E:\ProjData\Ethnic Enclaves, political participation\temp\hushåll88.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,065,671 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(76 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}65922             0
{txt}{hline 10}{c BT}{hline 27}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,912,957
{txt}{col 9}from master{col 30}{res}          21,549{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,891,408{txt}  (_merge==2)

{col 5}matched{col 30}{res}          44,383{txt}  (_merge==3)
{col 5}{hline 41}
(21,549 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId{p_end}
{txt}(4,601,698 missing values generated)
(2,334,093 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId im_year{p_end}
{txt}(4,588,252 missing values generated)
(13,446 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
(10,409 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,196,171
{txt}{col 9}from master{col 30}{res}       4,034,910{txt}  (_merge==1)
{col 9}from using{col 30}{res}       4,161,261{txt}  (_merge==2)

{col 5}matched{col 30}{res}         839,881{txt}  (_merge==3)
{col 5}{hline 41}
(8,196,171 observations deleted)

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}    573,832       68.32       68.32
{txt}        Utv {c |}{res}    266,049       31.68      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    839,881      100.00
{txt}(266,049 observations deleted)
Datum was {res:long} now {res:str8}
im_year2: all characters numeric; {res}replaced {txt}as {res}int
{txt}(93,692 observations deleted)
(459,847 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(35,854 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\tempfamily.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,143,966
{txt}{col 9}from master{col 30}{res}       4,143,966{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}matched{col 30}{res}         444,286{txt}  (_merge==3)
{col 5}{hline 41}
(19,444 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId{p_end}
{txt}(4,560,052 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
file E:\ProjData\Ethnic Enclaves, political participation\temp\hushåll89.dta saved

{com}. 
. 
. ///NOW REPEAT A SIMILAR CODE FOR 1990 AND 1991
> foreach y in 90 91 {c -(}
{txt}  2{com}. 
. use "RTB\migrationer"
{txt}  3{com}. 
. *Create immigration variable - keep immigrants
. 
. tab PostTyp, gen(m)
{txt}  4{com}.         keep if m1==1
{txt}  5{com}.         drop PostTyp m1 m2
{txt}  6{com}.  
. *Generate year, month of arrival - keep only year in question 
. 
. tostring Datum, replace
{txt}  7{com}. gen im_year = substr(Datum, 1, 4) 
{txt}  8{com}. destring im_year, replace
{txt}  9{com}. 
. gen im_month = substr(Datum, 5, 2) 
{txt} 10{com}. destring im_month, replace
{txt} 11{com}. 
.         keep if im_year==19`y'
{txt} 12{com}. 
. *small share who immigrated twise during a year
. 
. duplicates tag LopNr, gen(tag)
{txt} 13{com}. sort tag LopNr
{txt} 14{com}. 
. *Keep the observation from the latest immigration month
. bysort LopNr: egen latestmonth = max(im_month)
{txt} 15{com}.         drop if im_month!=latestmonth & tag>0
{txt} 16{com}.                 *duplicates report LopNr - OK
.         drop tag latestmonth
{txt} 17{com}. 
. ///NOW MERGE ON DATA WITH HOUSEHOLD VARIABLE
> 
. merge 1:m LopNr using "LISA\Lisa_19`y'"
{txt} 18{com}. drop if FamId==.        
{txt} 19{com}. 
. duplicates tag FamId, gen(tag_household)
{txt} 20{com}. 
. *SINGLES
. gen single = 1 if tag_household==0
{txt} 21{com}. drop if single==1
{txt} 22{com}. 
. *FAMILY with you:
. duplicates tag FamId im_year, gen(tag_hh2)
{txt} 23{com}. gen family_im = 1 if im_year==19`y' & tag_household>0 & tag_household==tag_hh2
{txt} 24{com}. drop if family_im==1
{txt} 25{com}. 
. *Family in the country
. gen anhorig = 1 if im_year==19`y'
{txt} 26{com}. 
. keep LopNr anhorig  
{txt} 27{com}. 
. *drop a very small number of duplicates
. duplicates drop LopNr, force
{txt} 28{com}. 
.         save "E:\ProjData\Ethnic Enclaves, political participation\temp\hushåll`y'", replace
{txt} 29{com}. {c )-}

{txt}Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,071,534 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(62 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,910,514
{txt}{col 9}from master{col 30}{res}          17,406{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,893,108{txt}  (_merge==2)

{col 5}matched{col 30}{res}          42,683{txt}  (_merge==3)
{col 5}{hline 41}
(17,406 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId{p_end}
{txt}(4,601,698 missing values generated)
(2,334,093 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId im_year{p_end}
{txt}(4,590,685 missing values generated)
(11,013 observations deleted)
(4,581,325 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
file E:\ProjData\Ethnic Enclaves, political participation\temp\hushåll90.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,081,864 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(37 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,943,922
{txt}{col 9}from master{col 30}{res}          14,175{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,929,747{txt}  (_merge==2)

{col 5}matched{col 30}{res}          35,632{txt}  (_merge==3)
{col 5}{hline 41}
(14,175 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId{p_end}
{txt}(4,591,993 missing values generated)
(2,373,386 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} FamId im_year{p_end}
{txt}(4,583,142 missing values generated)
(8,851 observations deleted)
(4,574,681 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(1 observation deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\hushåll91.dta saved

{com}. 
. 
. ///NOW PICK OUT THOSE WHO HAD SIBLINGS IN THE COUNTRY
> 
. *First create a file for everyone who immigrated in year`y'
. foreach y in 87 88 89 90 91 {c -(}
{txt}  2{com}. 
. use "RTB\migrationer"
{txt}  3{com}. 
. *Create immigration variable - keep immigrants
. 
. tab PostTyp, gen(m)
{txt}  4{com}.         keep if m1==1
{txt}  5{com}.         drop PostTyp m1 m2
{txt}  6{com}.  
. *Generate year, month of arrival - keep only year in question 
. 
. tostring Datum, replace
{txt}  7{com}. gen im_year = substr(Datum, 1, 4) 
{txt}  8{com}. destring im_year, replace
{txt}  9{com}. 
. gen im_month = substr(Datum, 5, 2) 
{txt} 10{com}. destring im_month, replace
{txt} 11{com}. 
.         keep if im_year==19`y'
{txt} 12{com}. 
. *small share who immigrated twise during a year
. 
. duplicates tag LopNr, gen(tag)
{txt} 13{com}. sort tag LopNr
{txt} 14{com}. 
. *Keep the observation from the latest immigration month
. bysort LopNr: egen latestmonth = max(im_month)
{txt} 15{com}.         drop if im_month!=latestmonth & tag>0
{txt} 16{com}.                 *duplicates report LopNr - OK
.         drop tag latestmonth
{txt} 17{com}.         
. save "E:\ProjData\Ethnic Enclaves, political participation\temp\syskon`y'", replace
{txt} 18{com}.         
.         
.         
. ///Now: sibling info and migration of the siblings
> ///start with a file definining only those living in the country in `y'
> use "RTB\RTB_19`y'"
{txt} 19{com}. 
. *file holds extremely few random duplicates - just drop - just need 1 obs per ind.
. duplicates drop LopNr, force
{txt} 20{com}. 
. *merge with data on migrations - keep only migrants
. merge 1:m LopNr using "RTB\migrationer"
{txt} 21{com}. keep if _merge==3
{txt} 22{com}. drop _merge
{txt} 23{com}. 
. *Create immigration variable to keep immigrants
. 
. tab PostTyp, gen(m)
{txt} 24{com}.         keep if m1==1
{txt} 25{com}.         drop PostTyp m1 m2
{txt} 26{com}.  
. *Generate year of arrival - keep earliest immigration year
. *only one migration per person
. tostring Datum, replace
{txt} 27{com}. gen im_year = substr(Datum, 1, 4) 
{txt} 28{com}. destring im_year, replace
{txt} 29{com}. 
. sort LopNr im_year
{txt} 30{com}. bysort LopNr: gen unique = _n
{txt} 31{com}. 
. keep if unique==1
{txt} 32{com}. 
. *rename id variable to note that these are the "siblings"
. *keep only id number an im year
. rename LopNr LopNrSyskon
{txt} 33{com}. keep LopNrSyskon im_year
{txt} 34{com}. 
. *Now merge on sibling; to get id number of the sibling immigrants       
. merge 1:m LopNrSyskon using "FlerGen\Syskon"
{txt} 35{com}. keep if _merge==3
{txt} 36{com}. drop _merge Syskontyp
{txt} 37{com}. 
. *Now we have data with all immigrants living in Sweden year `y' and merged on 
. *the id number of their sibling(s)
. *We can therefore merge on the info on all who imigrated in year `y', which we created earlier
. 
. merge m:1 LopNr using "E:\ProjData\Ethnic Enclaves, political participation\temp\syskon`y'"  
{txt} 38{com}. 
. keep if _merge==3
{txt} 39{com}. drop _merge
{txt} 40{com}. order LopNr
{txt} 41{com}. 
. *Only interested in siblings who immigrated before year `y'
. keep if im_year<19`y' 
{txt} 42{com}. 
. *Now: All individuals in the data immigrated in year `y', and had a resident sibling who immigrated an earlier year.
. 
. *drop duplicates cause ind might have sevewral siblings, and we only care if they has one or not.
. 
. duplicates drop LopNr, force
{txt} 43{com}. 
. gen sibling_im=1 
{txt} 44{com}. keep LopNr sibling_im
{txt} 45{com}. 
.         save "E:\ProjData\Ethnic Enclaves, political participation\temp\syskon`y'", replace
{txt} 46{com}. 
.         {c )-}       

{txt}Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,088,900 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(48 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon87.dta saved

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(12 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      10,868,976
{txt}{col 9}from master{col 30}{res}       7,449,242{txt}  (_merge==1)
{col 9}from using{col 30}{res}       3,419,734{txt}  (_merge==2)

{col 5}matched{col 30}{res}       1,581,408{txt}  (_merge==3)
{col 5}{hline 41}
(10,868,976 observations deleted)

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}    986,448       62.38       62.38
{txt}        Utv {c |}{res}    594,960       37.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  1,581,408      100.00
{txt}(594,960 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(130,838 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      18,721,126
{txt}{col 9}from master{col 30}{res}         477,057{txt}  (_merge==1)
{col 9}from using{col 30}{res}      18,244,069{txt}  (_merge==2)

{col 5}matched{col 30}{res}         929,569{txt}  (_merge==3)
{col 5}{hline 41}
(18,721,126 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}         945,324
{txt}{col 9}from master{col 30}{res}         911,973{txt}  (_merge==1)
{col 9}from using{col 30}{res}          33,351{txt}  (_merge==2)

{col 5}matched{col 30}{res}          17,596{txt}  (_merge==3)
{col 5}{hline 41}
(945,324 observations deleted)
(13,241 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(1,948 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon87.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,080,362 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(50 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon88.dta saved

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(30 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      10,820,367
{txt}{col 9}from master{col 30}{res}       7,457,229{txt}  (_merge==1)
{col 9}from using{col 30}{res}       3,363,138{txt}  (_merge==2)

{col 5}matched{col 30}{res}       1,638,004{txt}  (_merge==3)
{col 5}{hline 41}
(10,820,367 observations deleted)

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  1,027,736       62.74       62.74
{txt}        Utv {c |}{res}    610,268       37.26      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  1,638,004      100.00
{txt}(610,268 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(135,555 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      18,709,509
{txt}{col 9}from master{col 30}{res}         499,679{txt}  (_merge==1)
{col 9}from using{col 30}{res}      18,209,830{txt}  (_merge==2)

{col 5}matched{col 30}{res}         963,808{txt}  (_merge==3)
{col 5}{hline 41}
(18,709,509 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}         983,854
{txt}{col 9}from master{col 30}{res}         943,511{txt}  (_merge==1)
{col 9}from using{col 30}{res}          40,343{txt}  (_merge==2)

{col 5}matched{col 30}{res}          20,297{txt}  (_merge==3)
{col 5}{hline 41}
(983,854 observations deleted)
(15,841 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(1,916 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon88.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,065,671 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(76 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon89.dta saved

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(62 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      10,754,525
{txt}{col 9}from master{col 30}{res}       7,473,947{txt}  (_merge==1)
{col 9}from using{col 30}{res}       3,280,578{txt}  (_merge==2)

{col 5}matched{col 30}{res}       1,720,564{txt}  (_merge==3)
{col 5}{hline 41}
(10,754,525 observations deleted)

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  1,085,789       63.11       63.11
{txt}        Utv {c |}{res}    634,775       36.89      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  1,720,564      100.00
{txt}(634,775 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(142,541 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      18,693,624
{txt}{col 9}from master{col 30}{res}         532,034{txt}  (_merge==1)
{col 9}from using{col 30}{res}      18,161,590{txt}  (_merge==2)

{col 5}matched{col 30}{res}       1,012,048{txt}  (_merge==3)
{col 5}{hline 41}
(18,693,624 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       1,035,621
{txt}{col 9}from master{col 30}{res}         983,909{txt}  (_merge==1)
{col 9}from using{col 30}{res}          51,712{txt}  (_merge==2)

{col 5}matched{col 30}{res}          28,139{txt}  (_merge==3)
{col 5}{hline 41}
(1,035,621 observations deleted)
(23,369 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(2,011 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon89.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,071,534 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(62 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon90.dta saved

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(90 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      10,713,344
{txt}{col 9}from master{col 30}{res}       7,495,549{txt}  (_merge==1)
{col 9}from using{col 30}{res}       3,217,795{txt}  (_merge==2)

{col 5}matched{col 30}{res}       1,783,347{txt}  (_merge==3)
{col 5}{hline 41}
(10,713,344 observations deleted)

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  1,132,168       63.49       63.49
{txt}        Utv {c |}{res}    651,179       36.51      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  1,783,347      100.00
{txt}(651,179 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(147,355 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      18,673,504
{txt}{col 9}from master{col 30}{res}         557,207{txt}  (_merge==1)
{col 9}from using{col 30}{res}      18,116,297{txt}  (_merge==2)

{col 5}matched{col 30}{res}       1,057,341{txt}  (_merge==3)
{col 5}{hline 41}
(18,673,504 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       1,076,147
{txt}{col 9}from master{col 30}{res}       1,029,309{txt}  (_merge==1)
{col 9}from using{col 30}{res}          46,838{txt}  (_merge==2)

{col 5}matched{col 30}{res}          28,032{txt}  (_merge==3)
{col 5}{hline 41}
(1,076,147 observations deleted)
(23,094 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(2,139 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon90.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,081,864 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(37 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon91.dta saved

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(207 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      10,696,771
{txt}{col 9}from master{col 30}{res}       7,517,553{txt}  (_merge==1)
{col 9}from using{col 30}{res}       3,179,218{txt}  (_merge==2)

{col 5}matched{col 30}{res}       1,821,924{txt}  (_merge==3)
{col 5}{hline 41}
(10,696,771 observations deleted)

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  1,165,033       63.95       63.95
{txt}        Utv {c |}{res}    656,891       36.05      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  1,821,924      100.00
{txt}(656,891 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(149,268 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      18,655,002
{txt}{col 9}from master{col 30}{res}         575,730{txt}  (_merge==1)
{col 9}from using{col 30}{res}      18,079,272{txt}  (_merge==2)

{col 5}matched{col 30}{res}       1,094,366{txt}  (_merge==3)
{col 5}{hline 41}
(18,655,002 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       1,107,675
{txt}{col 9}from master{col 30}{res}       1,069,299{txt}  (_merge==1)
{col 9}from using{col 30}{res}          38,376{txt}  (_merge==2)

{col 5}matched{col 30}{res}          25,067{txt}  (_merge==3)
{col 5}{hline 41}
(1,107,675 observations deleted)
(20,689 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(1,950 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\syskon91.dta saved

{com}.         
.         
. 
. *log close
. 
{txt}end of do-file

{com}. 
. 
. ///PREPARE OTHER DATA
> *Prepares the enclaves variables (share of coethnics on different geographical levels)
. *Also prepares some other data: moving between municipalities and voting for radical right 1991
. 
. do "C:\Userdata\Shared\Dofiles\DoData\Ethnic Enclaves, political participation\enclaves"
{txt}
{com}. *******************************************************
. * 2. CREATE ENCLAVE SIZES AND PREPARE SOME OTHER DATA *
. ******************************************************
. 
. *This do-file prepares some files used in the analysis
. *most importantly the enclave files: share of coethnics in parish
. *also: some info used on additional analysis: vote for radical right party 1991 (heterogeneity) and
. *moving between municipalities (used as additional control)
. 
. ///SET DIRECTORY/log-files/set more off
> 
. clear all
{res}{txt}
{com}. cd "D:\SCB_ConPol\Stata"
{res}D:\SCB_ConPol\Stata
{txt}
{com}. set more off
{txt}
{com}. 
. *capture log close
. *log using "C:\Userdata\Shared\Logs\Ethnic Enclaves, political participation\log_enclaves", replace
. 
. 
. 
. ///ENCLAVES MEASURES - SHARE OF COETHNICS IN PARISH
> 
. ///small preperation of ses data that can be dropped at the end of the do-file
> *creates the socioeconomic measures from 1985 that can be merged on to the 1987,1988,1999 cohorts
. use "E:\ProjData\SESData\ses-SESData"
{txt}
{com}. keep if Ar==1985
{txt}(48,380,386 observations deleted)

{com}. keep LopNr SamRakInk UtbNiva treiman
{txt}
{com}. *4 duplicates; delete
. duplicates drop LopNr, force

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(4 observations deleted)

{com}. save "E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1987temp", replace
{txt}(note: file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1987temp.dta not found)
file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1987temp.dta saved

{com}. save "E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1988temp", replace
{txt}(note: file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1988temp.dta not found)
file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1988temp.dta saved

{com}. save "E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1989temp", replace
{txt}(note: file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1989temp.dta not found)
file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1989temp.dta saved

{com}. clear
{txt}
{com}. 
. ///Create the socioeconomic measures from 1990 that can be merged on to the 1990,1991 cohorts 
> use "E:\ProjData\SESData\ses-SESData"
{txt}
{com}. keep if Ar==1990
{txt}(48,153,320 observations deleted)

{com}. keep LopNr SamRakInk UtbNiva treiman
{txt}
{com}. *87 duplicates; delete
. duplicates drop LopNr, force

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(87 observations deleted)

{com}. save "E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1990temp", replace
{txt}(note: file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1990temp.dta not found)
file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1990temp.dta saved

{com}. save "E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1991temp", replace
{txt}(note: file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1991temp.dta not found)
file E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure1991temp.dta saved

{com}. clear
{txt}
{com}. 
. 
. ///NOW CREATE THE ENCLAVE MEASURES FOR EACH YEAR
> foreach y in 87 88 89 90 91 {c -(}
{txt}  2{com}. 
. ///first open and prepare migration-files
> *to pick out those who migrated year `y' is useful to know how many in the enclave that 
. *were in the same cohort as the immigrants we are analysing, we are, however,
. *currently not using this info
. 
. use "RTB\migrationer"
{txt}  3{com}. 
. *Create immigration variable - keep immigrants
. 
. tab PostTyp, gen(m)
{txt}  4{com}.         keep if m1==1
{txt}  5{com}.         drop PostTyp m1 m2
{txt}  6{com}.  
. *Generate year, month of arrival - keep only year in question 
. 
. tostring Datum, replace
{txt}  7{com}. gen im_year = substr(Datum, 1, 4) 
{txt}  8{com}. destring im_year, replace
{txt}  9{com}. 
. gen im_month = substr(Datum, 5, 2) 
{txt} 10{com}. destring im_month, replace
{txt} 11{com}. 
.         keep if im_year==19`y'
{txt} 12{com}. 
. *small share who immigrated twise during a year
. *Keep the observation from the latest immigration month
. 
. duplicates tag LopNr, gen(tag)
{txt} 13{com}. sort tag LopNr
{txt} 14{com}. 
. bysort LopNr: egen latestmonth = max(im_month)
{txt} 15{com}.         drop if im_month!=latestmonth & tag>0
{txt} 16{com}.                 *duplicates report LopNr - OK
.         drop tag latestmonth
{txt} 17{com}. 
. ///HAVING PREPARED THOSE WHO IMMIGRATED IN `Y', WE MERGE WITH IND-DATA ON ALL WHO LIVE IN THE COUNTRY
> 
. *Use 1:m beacause there are some id-number duplicates in RTB-file
. merge 1:m LopNr using "RTB\RTB_19`y'"
{txt} 18{com}.         drop if _merge==1 /*Not left in th country at the end of the year*/
{txt} 19{com}.         drop _merge
{txt} 20{com}. 
. *Small number of duplicates remaining - throw out one obs.
. duplicates drop LopNr, force
{txt} 21{com}. 
. ///MERGE WITH BIRTH COUNTRY DATA
> merge 1:m LopNr using "RTB\FodelselandIFAUGrupp"
{txt} 22{com}.         drop _merge 
{txt} 23{com}.         keep if Kommun!=. /*Keep only residents in that year*/
{txt} 24{com}.         *duplicates report LopNr - OK
. 
. ***Now create fill in the information on country of birth (labels)
. 
. lab def IFAUkod 0 "Sweden"
{txt} 25{com}. lab def IFAUkod 26 "Finland", add
{txt} 26{com}. lab def IFAUkod 27 "Denmark", add
{txt} 27{com}. lab def IFAUkod 28 "Norway & Iceland", add
{txt} 28{com}. lab def IFAUkod 29 "Bosnia", add
{txt} 29{com}. lab def IFAUkod 30 "Yugoslavia", add
{txt} 30{com}. lab def IFAUkod 31 "Poland", add
{txt} 31{com}. lab def IFAUkod 32 "UK & Ireland", add
{txt} 32{com}. lab def IFAUkod 33 "Germany", add
{txt} 33{com}. lab def IFAUkod 34 "Mediterranian", add
{txt} 34{com}. lab def IFAUkod 35 "Baltic", add
{txt} 35{com}. lab def IFAUkod 36 "East Europe", add
{txt} 36{com}. lab def IFAUkod 37 "Hungary & Czech & Slovakia", add
{txt} 37{com}. lab def IFAUkod 38 "Benelux & Swizz & Austria", add
{txt} 38{com}. lab def IFAUkod 39 "US & Canada", add
{txt} 39{com}. lab def IFAUkod 40 "Central America", add
{txt} 40{com}. lab def IFAUkod 41 "Chile", add
{txt} 41{com}. lab def IFAUkod 42 "South America", add
{txt} 42{com}. lab def IFAUkod 43 "East Africa", add
{txt} 43{com}. lab def IFAUkod 44 "Mena", add
{txt} 44{com}. lab def IFAUkod 45 "Other Africa", add
{txt} 45{com}. lab def IFAUkod 46 "Iran", add
{txt} 46{com}. lab def IFAUkod 47 "Iraq", add  
{txt} 47{com}. lab def IFAUkod 48 "Turkey", add
{txt} 48{com}. lab def IFAUkod 49 "East Asia", add
{txt} 49{com}. lab def IFAUkod 50 "South East Asia", add
{txt} 50{com}. lab def IFAUkod 51 "South Asia", add
{txt} 51{com}. lab def IFAUkod 52 "Oceania", add
{txt} 52{com}. lab def IFAUkod 53 "Unknown or Stateless", add
{txt} 53{com}. 
. lab values IFAUkod IFAUkod      
{txt} 54{com}. 
. tab IFAUkod, sort
{txt} 55{com}. 
. 
. ///NOW WANT TO CREATE THE NUMBER OF COETHNICS ON PARISH LEVEL- 
> 
. *all coethnics
. tab IFAUkod, gen(country)
{txt} 56{com}. foreach k of numlist 1(1)29 {c -(}
{txt} 57{com}. replace country`k' = . if country`k'==0
{txt} 58{com}. {c )-}
{txt} 59{com}. 
. ///those who came the same year
> tab IFAUkod if im_year==19`y', gen(im)
{txt} 60{com}. *29 doesnt exist for 1987
. capture gen im29=.
{txt} 61{com}. foreach l of numlist 1(1)29 {c -(}
{txt} 62{com}. replace im`l' = . if im`l'==0
{txt} 63{com}. {c )-}
{txt} 64{com}. 
. ///population
> gen pop =1 
{txt} 65{com}. 
. *Number of foreign born generally
. gen foreign = 1 if IFAUkod!=0
{txt} 66{com}. 
. 
. ///politically integrated  - share who voted in election in 1982
> 
. merge 1:m LopNr using "VD/Valdelt_82"
{txt} 67{com}. drop _merge
{txt} 68{com}. 
. duplicates drop LopNr, force 
{txt} 69{com}. 
. *variable for voted in municipal election 1982
. gen votedk = 1 if k==2 | k==4 | k==5 | k==6
{txt} 70{com}. 
. *right to vote
. gen rightk = 1 if k==2 | k==1 | k==4 | k==5 | k==6
{txt} 71{com}. 
. *create the immigration variables to collapse
. tab IFAUkod if votedk==1, gen(voted)
{txt} 72{com}. foreach h of numlist 1(1)29 {c -(}
{txt} 73{com}. replace voted`h' = . if voted`h'==0
{txt} 74{com}. {c )-}
{txt} 75{com}. 
. tab IFAUkod if rightk==1, gen(right)
{txt} 76{com}. foreach a of numlist 1(1)29 {c -(}
{txt} 77{com}. replace right`a' = . if right`a'==0
{txt} 78{com}. {c )-}
{txt} 79{com}. 
. 
. ///economically integrated  - ses measures 
> 
. merge 1:1 LopNr using "E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure19`y'temp"
{txt} 80{com}. drop if _merge==2 /*drop those not in the main data*/
{txt} 81{com}. drop _merge
{txt} 82{com}. 
. ///treiman
> foreach b of numlist 1(1)29 {c -(}
{txt} 83{com}. gen treiman`b' = treiman if country`b'==1
{txt} 84{com}. {c )-}
{txt} 85{com}. 
. ///total income
> 
. foreach g of numlist 1(1)29 {c -(}
{txt} 86{com}. gen inc`g' = SamRakInk if country`g'==1
{txt} 87{com}. {c )-}
{txt} 88{com}. 
. ///Education
> gen high_educ = 1 if UtbNiva>3 
{txt} 89{com}. replace high_educ=. if UtbNiva==.
{txt} 90{com}. 
. tab IFAUkod if high_educ==1, gen(educ)
{txt} 91{com}. foreach r of numlist 1(1)29 {c -(}
{txt} 92{com}. replace educ`r' = . if educ`r'==0
{txt} 93{com}. {c )-}
{txt} 94{com}. 
. collapse (count) pop foreign votedk rightk im1 im2 im3 im4 im5 im6 im7 im8 im9 im10 im11 im12 im13 im14 ///
> im15 im16 im17 im18 im19 im20 im21 im22 im23 im24 im25 im26 im27 im28 im29 /// 
> country1 country2 country3 country4 country5 country6 country7 country8 country9 ///
> country10 country11 country12 country13 country14 country15 country16 country17 /// 
> country18 country19 country20 country21 country22 country23 country24 country25 ///
> country26 country27 country28 country29 voted1 voted2 voted3 voted4 voted5 voted6 ///
> voted7 voted8 voted9 voted10 voted11 voted12 voted13 voted14 voted15 voted16 /// 
> voted17 voted18 voted19 voted20 voted21 voted22 voted23 voted24 voted25 voted26 /// 
> voted27 voted28 voted29 right1 right2 right3 right4 right5 right6 right7 right8 ///
> right9 right10 right11 right12 right13 right14 right15 right16 right17 right18 /// 
> right19 right20 right21 right22 right23 right24 right25 right26 right27 right28 ///
> right29 educ1 educ2 educ3 educ4 educ5 educ6 educ7 educ8 ///
> educ9 educ10 educ11 educ12 educ13 educ14 educ15 educ16 educ17 educ18 /// 
> educ19 educ20 educ21 educ22 educ23 educ24 educ25 educ26 educ27 educ28 ///
> educ29 (mean) treiman1 treiman2 treiman3 treiman4 treiman5 treiman6 treiman7 /// 
> treiman8 treiman9 treiman10 treiman11 treiman12 treiman13 treiman14 treiman15 /// 
> treiman16 treiman17 treiman18 treiman19 treiman20 treiman21 treiman22 treiman23 /// 
> treiman24 treiman25 treiman26 treiman27 treiman28 treiman29 inc1 inc2 inc3 inc4 inc5 inc6 inc7 inc8 inc9 inc10 inc11 inc12 inc13 inc14 ///
> inc15 inc16 inc17 inc18 inc19 inc20 inc21 inc22 inc23 inc24 inc25 inc26 inc27 inc28 inc29, by(Forsamling)
{txt} 95{com}. 
. save "E:\ProjData\Ethnic Enclaves, political participation\temp\enclave_parish`y'", replace
{txt} 96{com}. 
. {c )-}

{txt}Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,088,900 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(48 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,373,758
{txt}{col 9}from master{col 30}{res}           1,197{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,372,561{txt}  (_merge==2)

{col 5}matched{col 30}{res}          41,528{txt}  (_merge==3)
{col 5}{hline 41}
(1,197 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(12 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,658,456
{txt}{col 9}from master{col 30}{res}             279{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,658,177{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,413,798{txt}  (_merge==3)
{col 5}{hline 41}
(7,658,177 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,724,147       91.81       91.81
{txt}                   Finland {c |}{res}    223,857        2.66       94.47
{txt}          Norway & Iceland {c |}{res}     46,324        0.55       95.02
{txt}                   Denmark {c |}{res}     40,634        0.48       95.50
{txt}                Yugoslavia {c |}{res}     38,681        0.46       95.96
{txt}                   Germany {c |}{res}     37,833        0.45       96.41
{txt}                    Poland {c |}{res}     31,294        0.37       96.78
{txt}             Mediterranian {c |}{res}     26,225        0.31       97.10
{txt}Hungary & Czech & Slovakia {c |}{res}     21,737        0.26       97.35
{txt}                    Turkey {c |}{res}     21,096        0.25       97.61
{txt}                      Iran {c |}{res}     21,059        0.25       97.86
{txt}                      Mena {c |}{res}     19,175        0.23       98.08
{txt}                     Chile {c |}{res}     17,843        0.21       98.30
{txt} Benelux & Swizz & Austria {c |}{res}     16,101        0.19       98.49
{txt}                    Baltic {c |}{res}     15,443        0.18       98.67
{txt}                South Asia {c |}{res}     15,193        0.18       98.85
{txt}             South America {c |}{res}     13,925        0.17       99.02
{txt}               US & Canada {c |}{res}     13,587        0.16       99.18
{txt}               East Europe {c |}{res}     12,760        0.15       99.33
{txt}                 East Asia {c |}{res}     12,424        0.15       99.48
{txt}           South East Asia {c |}{res}     11,652        0.14       99.62
{txt}              UK & Ireland {c |}{res}     10,478        0.12       99.74
{txt}              Other Africa {c |}{res}      6,077        0.07       99.81
{txt}               East Africa {c |}{res}      5,542        0.07       99.88
{txt}                      Iraq {c |}{res}      5,200        0.06       99.94
{txt}           Central America {c |}{res}      3,017        0.04       99.98
{txt}                   Oceania {c |}{res}      1,320        0.02       99.99
{txt}                    Bosnia {c |}{res}        653        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         50        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,413,327      100.00

    {txt}Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,724,147       91.81       91.81
{txt}                   Finland {c |}{res}    223,857        2.66       94.47
{txt}                   Denmark {c |}{res}     40,634        0.48       94.95
{txt}          Norway & Iceland {c |}{res}     46,324        0.55       95.50
{txt}                    Bosnia {c |}{res}        653        0.01       95.51
{txt}                Yugoslavia {c |}{res}     38,681        0.46       95.97
{txt}                    Poland {c |}{res}     31,294        0.37       96.34
{txt}              UK & Ireland {c |}{res}     10,478        0.12       96.47
{txt}                   Germany {c |}{res}     37,833        0.45       96.92
{txt}             Mediterranian {c |}{res}     26,225        0.31       97.23
{txt}                    Baltic {c |}{res}     15,443        0.18       97.41
{txt}               East Europe {c |}{res}     12,760        0.15       97.56
{txt}Hungary & Czech & Slovakia {c |}{res}     21,737        0.26       97.82
{txt} Benelux & Swizz & Austria {c |}{res}     16,101        0.19       98.01
{txt}               US & Canada {c |}{res}     13,587        0.16       98.17
{txt}           Central America {c |}{res}      3,017        0.04       98.21
{txt}                     Chile {c |}{res}     17,843        0.21       98.42
{txt}             South America {c |}{res}     13,925        0.17       98.59
{txt}               East Africa {c |}{res}      5,542        0.07       98.65
{txt}                      Mena {c |}{res}     19,175        0.23       98.88
{txt}              Other Africa {c |}{res}      6,077        0.07       98.95
{txt}                      Iran {c |}{res}     21,059        0.25       99.20
{txt}                      Iraq {c |}{res}      5,200        0.06       99.27
{txt}                    Turkey {c |}{res}     21,096        0.25       99.52
{txt}                 East Asia {c |}{res}     12,424        0.15       99.66
{txt}           South East Asia {c |}{res}     11,652        0.14       99.80
{txt}                South Asia {c |}{res}     15,193        0.18       99.98
{txt}                   Oceania {c |}{res}      1,320        0.02      100.00
{txt}      Unknown or Stateless {c |}{res}         50        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,413,327      100.00
{txt}(689,180 real changes made, 689,180 to missing)
(8,189,470 real changes made, 8,189,470 to missing)
(8,372,693 real changes made, 8,372,693 to missing)
(8,367,003 real changes made, 8,367,003 to missing)
(8,412,674 real changes made, 8,412,674 to missing)
(8,374,646 real changes made, 8,374,646 to missing)
(8,382,033 real changes made, 8,382,033 to missing)
(8,402,849 real changes made, 8,402,849 to missing)
(8,375,494 real changes made, 8,375,494 to missing)
(8,387,102 real changes made, 8,387,102 to missing)
(8,397,884 real changes made, 8,397,884 to missing)
(8,400,567 real changes made, 8,400,567 to missing)
(8,391,590 real changes made, 8,391,590 to missing)
(8,397,226 real changes made, 8,397,226 to missing)
(8,399,740 real changes made, 8,399,740 to missing)
(8,410,310 real changes made, 8,410,310 to missing)
(8,395,484 real changes made, 8,395,484 to missing)
(8,399,402 real changes made, 8,399,402 to missing)
(8,407,785 real changes made, 8,407,785 to missing)
(8,394,152 real changes made, 8,394,152 to missing)
(8,407,250 real changes made, 8,407,250 to missing)
(8,392,268 real changes made, 8,392,268 to missing)
(8,408,127 real changes made, 8,408,127 to missing)
(8,392,231 real changes made, 8,392,231 to missing)
(8,400,903 real changes made, 8,400,903 to missing)
(8,401,675 real changes made, 8,401,675 to missing)
(8,398,134 real changes made, 8,398,134 to missing)
(8,412,007 real changes made, 8,412,007 to missing)
(8,413,277 real changes made, 8,413,277 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}      4,927       11.88       11.88
{txt}                   Finland {c |}{res}      4,201       10.13       22.00
{txt}                   Denmark {c |}{res}      1,099        2.65       24.65
{txt}          Norway & Iceland {c |}{res}      2,546        6.14       30.79
{txt}                    Bosnia {c |}{res}         24        0.06       30.85
{txt}                Yugoslavia {c |}{res}        942        2.27       33.12
{txt}                    Poland {c |}{res}      1,597        3.85       36.96
{txt}              UK & Ireland {c |}{res}        760        1.83       38.80
{txt}                   Germany {c |}{res}        625        1.51       40.30
{txt}             Mediterranian {c |}{res}        863        2.08       42.38
{txt}                    Baltic {c |}{res}         40        0.10       42.48
{txt}               East Europe {c |}{res}      1,156        2.79       45.27
{txt}Hungary & Czech & Slovakia {c |}{res}        559        1.35       46.61
{txt} Benelux & Swizz & Austria {c |}{res}        614        1.48       48.09
{txt}               US & Canada {c |}{res}        951        2.29       50.39
{txt}           Central America {c |}{res}        353        0.85       51.24
{txt}                     Chile {c |}{res}      2,767        6.67       57.91
{txt}             South America {c |}{res}      1,073        2.59       60.49
{txt}               East Africa {c |}{res}      1,290        3.11       63.60
{txt}                      Mena {c |}{res}      2,104        5.07       68.67
{txt}              Other Africa {c |}{res}        668        1.61       70.28
{txt}                      Iran {c |}{res}      7,397       17.83       88.11
{txt}                      Iraq {c |}{res}        442        1.07       89.18
{txt}                    Turkey {c |}{res}      1,313        3.16       92.34
{txt}                 East Asia {c |}{res}        903        2.18       94.52
{txt}           South East Asia {c |}{res}        915        2.21       96.72
{txt}                South Asia {c |}{res}      1,149        2.77       99.49
{txt}                   Oceania {c |}{res}        210        0.51      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     41,488      100.00
{txt}(36,561 real changes made, 36,561 to missing)
(37,287 real changes made, 37,287 to missing)
(40,389 real changes made, 40,389 to missing)
(38,942 real changes made, 38,942 to missing)
(41,464 real changes made, 41,464 to missing)
(40,546 real changes made, 40,546 to missing)
(39,891 real changes made, 39,891 to missing)
(40,728 real changes made, 40,728 to missing)
(40,863 real changes made, 40,863 to missing)
(40,625 real changes made, 40,625 to missing)
(41,448 real changes made, 41,448 to missing)
(40,332 real changes made, 40,332 to missing)
(40,929 real changes made, 40,929 to missing)
(40,874 real changes made, 40,874 to missing)
(40,537 real changes made, 40,537 to missing)
(41,135 real changes made, 41,135 to missing)
(38,721 real changes made, 38,721 to missing)
(40,415 real changes made, 40,415 to missing)
(40,198 real changes made, 40,198 to missing)
(39,384 real changes made, 39,384 to missing)
(40,820 real changes made, 40,820 to missing)
(34,091 real changes made, 34,091 to missing)
(41,046 real changes made, 41,046 to missing)
(40,175 real changes made, 40,175 to missing)
(40,585 real changes made, 40,585 to missing)
(40,573 real changes made, 40,573 to missing)
(40,339 real changes made, 40,339 to missing)
(41,278 real changes made, 41,278 to missing)
(0 real changes made)
(7,724,147 missing values generated)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       3,461,403
{txt}{col 9}from master{col 30}{res}       2,948,551{txt}  (_merge==1)
{col 9}from using{col 30}{res}         512,852{txt}  (_merge==2)

{col 5}matched{col 30}{res}       5,465,528{txt}  (_merge==3)
{col 5}{hline 41}

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(2 observations deleted)
(3,670,828 missing values generated)
(3,056,794 missing values generated)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,568,555       93.51       93.51
{txt}                   Finland {c |}{res}    125,635        2.57       96.08
{txt}                   Denmark {c |}{res}     22,883        0.47       96.55
{txt}          Norway & Iceland {c |}{res}     24,303        0.50       97.05
{txt}                    Bosnia {c |}{res}        284        0.01       97.06
{txt}                Yugoslavia {c |}{res}     18,282        0.37       97.43
{txt}                    Poland {c |}{res}     11,519        0.24       97.67
{txt}              UK & Ireland {c |}{res}      3,359        0.07       97.73
{txt}                   Germany {c |}{res}     25,570        0.52       98.26
{txt}             Mediterranian {c |}{res}     13,588        0.28       98.54
{txt}                    Baltic {c |}{res}     12,674        0.26       98.79
{txt}               East Europe {c |}{res}      5,882        0.12       98.92
{txt}Hungary & Czech & Slovakia {c |}{res}     13,372        0.27       99.19
{txt} Benelux & Swizz & Austria {c |}{res}      9,010        0.18       99.37
{txt}               US & Canada {c |}{res}      6,702        0.14       99.51
{txt}           Central America {c |}{res}        416        0.01       99.52
{txt}                     Chile {c |}{res}      3,037        0.06       99.58
{txt}             South America {c |}{res}      2,841        0.06       99.64
{txt}               East Africa {c |}{res}        581        0.01       99.65
{txt}                      Mena {c |}{res}      4,001        0.08       99.73
{txt}              Other Africa {c |}{res}      1,615        0.03       99.77
{txt}                      Iran {c |}{res}        817        0.02       99.78
{txt}                      Iraq {c |}{res}        253        0.01       99.79
{txt}                    Turkey {c |}{res}      5,757        0.12       99.91
{txt}                 East Asia {c |}{res}      1,298        0.03       99.93
{txt}           South East Asia {c |}{res}      1,122        0.02       99.96
{txt}                South Asia {c |}{res}      1,887        0.04       99.99
{txt}                   Oceania {c |}{res}        259        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         21        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  4,885,523      100.00
{txt}(316,968 real changes made, 316,968 to missing)
(4,759,888 real changes made, 4,759,888 to missing)
(4,862,640 real changes made, 4,862,640 to missing)
(4,861,220 real changes made, 4,861,220 to missing)
(4,885,239 real changes made, 4,885,239 to missing)
(4,867,241 real changes made, 4,867,241 to missing)
(4,874,004 real changes made, 4,874,004 to missing)
(4,882,164 real changes made, 4,882,164 to missing)
(4,859,953 real changes made, 4,859,953 to missing)
(4,871,935 real changes made, 4,871,935 to missing)
(4,872,849 real changes made, 4,872,849 to missing)
(4,879,641 real changes made, 4,879,641 to missing)
(4,872,151 real changes made, 4,872,151 to missing)
(4,876,513 real changes made, 4,876,513 to missing)
(4,878,821 real changes made, 4,878,821 to missing)
(4,885,107 real changes made, 4,885,107 to missing)
(4,882,486 real changes made, 4,882,486 to missing)
(4,882,682 real changes made, 4,882,682 to missing)
(4,884,942 real changes made, 4,884,942 to missing)
(4,881,522 real changes made, 4,881,522 to missing)
(4,883,908 real changes made, 4,883,908 to missing)
(4,884,706 real changes made, 4,884,706 to missing)
(4,885,270 real changes made, 4,885,270 to missing)
(4,879,766 real changes made, 4,879,766 to missing)
(4,884,225 real changes made, 4,884,225 to missing)
(4,884,401 real changes made, 4,884,401 to missing)
(4,883,636 real changes made, 4,883,636 to missing)
(4,885,264 real changes made, 4,885,264 to missing)
(4,885,502 real changes made, 4,885,502 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,930,531       92.02       92.02
{txt}                   Finland {c |}{res}    175,277        3.27       95.29
{txt}                   Denmark {c |}{res}     30,708        0.57       95.87
{txt}          Norway & Iceland {c |}{res}     31,505        0.59       96.45
{txt}                    Bosnia {c |}{res}        480        0.01       96.46
{txt}                Yugoslavia {c |}{res}     29,563        0.55       97.01
{txt}                    Poland {c |}{res}     15,173        0.28       97.30
{txt}              UK & Ireland {c |}{res}      5,073        0.09       97.39
{txt}                   Germany {c |}{res}     29,880        0.56       97.95
{txt}             Mediterranian {c |}{res}     18,728        0.35       98.30
{txt}                    Baltic {c |}{res}     14,058        0.26       98.56
{txt}               East Europe {c |}{res}      7,091        0.13       98.69
{txt}Hungary & Czech & Slovakia {c |}{res}     16,140        0.30       98.99
{txt} Benelux & Swizz & Austria {c |}{res}     11,192        0.21       99.20
{txt}               US & Canada {c |}{res}      8,063        0.15       99.35
{txt}           Central America {c |}{res}        616        0.01       99.37
{txt}                     Chile {c |}{res}      3,677        0.07       99.43
{txt}             South America {c |}{res}      3,689        0.07       99.50
{txt}               East Africa {c |}{res}        940        0.02       99.52
{txt}                      Mena {c |}{res}      6,080        0.11       99.63
{txt}              Other Africa {c |}{res}      2,212        0.04       99.68
{txt}                      Iran {c |}{res}      1,554        0.03       99.70
{txt}                      Iraq {c |}{res}        390        0.01       99.71
{txt}                    Turkey {c |}{res}      8,593        0.16       99.87
{txt}                 East Asia {c |}{res}      2,172        0.04       99.91
{txt}           South East Asia {c |}{res}      1,681        0.03       99.94
{txt}                South Asia {c |}{res}      2,614        0.05       99.99
{txt}                   Oceania {c |}{res}        346        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         36        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  5,358,062      100.00
{txt}(427,531 real changes made, 427,531 to missing)
(5,182,785 real changes made, 5,182,785 to missing)
(5,327,354 real changes made, 5,327,354 to missing)
(5,326,557 real changes made, 5,326,557 to missing)
(5,357,582 real changes made, 5,357,582 to missing)
(5,328,499 real changes made, 5,328,499 to missing)
(5,342,889 real changes made, 5,342,889 to missing)
(5,352,989 real changes made, 5,352,989 to missing)
(5,328,182 real changes made, 5,328,182 to missing)
(5,339,334 real changes made, 5,339,334 to missing)
(5,344,004 real changes made, 5,344,004 to missing)
(5,350,971 real changes made, 5,350,971 to missing)
(5,341,922 real changes made, 5,341,922 to missing)
(5,346,870 real changes made, 5,346,870 to missing)
(5,349,999 real changes made, 5,349,999 to missing)
(5,357,446 real changes made, 5,357,446 to missing)
(5,354,385 real changes made, 5,354,385 to missing)
(5,354,373 real changes made, 5,354,373 to missing)
(5,357,122 real changes made, 5,357,122 to missing)
(5,351,982 real changes made, 5,351,982 to missing)
(5,355,850 real changes made, 5,355,850 to missing)
(5,356,508 real changes made, 5,356,508 to missing)
(5,357,672 real changes made, 5,357,672 to missing)
(5,349,469 real changes made, 5,349,469 to missing)
(5,355,890 real changes made, 5,355,890 to missing)
(5,356,381 real changes made, 5,356,381 to missing)
(5,355,448 real changes made, 5,355,448 to missing)
(5,357,716 real changes made, 5,357,716 to missing)
(5,358,026 real changes made, 5,358,026 to missing)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}         650,821
{txt}{col 9}from master{col 30}{res}         611,584{txt}  (_merge==1)
{col 9}from using{col 30}{res}          39,237{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,315,345{txt}  (_merge==3)
{col 5}{hline 41}
(39,237 observations deleted)
(5,848,306 missing values generated)
(8,799,736 missing values generated)
(8,907,470 missing values generated)
(8,909,049 missing values generated)
(8,926,580 missing values generated)
(8,906,521 missing values generated)
(8,915,060 missing values generated)
(8,922,271 missing values generated)
(8,907,192 missing values generated)
(8,914,032 missing values generated)
(8,920,679 missing values generated)
(8,923,038 missing values generated)
(8,915,886 missing values generated)
(8,918,973 missing values generated)
(8,922,445 missing values generated)
(8,926,389 missing values generated)
(8,922,663 missing values generated)
(8,923,597 missing values generated)
(8,925,857 missing values generated)
(8,921,817 missing values generated)
(8,925,124 missing values generated)
(8,925,327 missing values generated)
(8,925,981 missing values generated)
(8,920,119 missing values generated)
(8,924,803 missing values generated)
(8,924,153 missing values generated)
(8,924,349 missing values generated)
(8,926,576 missing values generated)
(8,926,913 missing values generated)
(1,432,526 missing values generated)
(8,710,690 missing values generated)
(8,888,173 missing values generated)
(8,884,970 missing values generated)
(8,926,317 missing values generated)
(8,890,234 missing values generated)
(8,899,117 missing values generated)
(8,917,857 missing values generated)
(8,890,356 missing values generated)
(8,902,358 missing values generated)
(8,911,572 missing values generated)
(8,916,446 missing values generated)
(8,906,248 missing values generated)
(8,911,948 missing values generated)
(8,915,106 missing values generated)
(8,924,641 missing values generated)
(8,914,055 missing values generated)
(8,914,917 missing values generated)
(8,923,418 missing values generated)
(8,912,463 missing values generated)
(8,922,032 missing values generated)
(8,918,542 missing values generated)
(8,922,968 missing values generated)
(8,908,360 missing values generated)
(8,916,317 missing values generated)
(8,917,054 missing values generated)
(8,914,127 missing values generated)
(8,925,941 missing values generated)
(8,926,882 missing values generated)
(4,491,327 missing values generated)
(2,596,956 real changes made, 2,596,956 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  1,684,607       91.71       91.71
{txt}                   Finland {c |}{res}     40,740        2.22       93.93
{txt}                   Denmark {c |}{res}      8,116        0.44       94.37
{txt}          Norway & Iceland {c |}{res}      8,341        0.45       94.83
{txt}                    Bosnia {c |}{res}        118        0.01       94.83
{txt}                Yugoslavia {c |}{res}      7,976        0.43       95.27
{txt}                    Poland {c |}{res}     10,939        0.60       95.86
{txt}              UK & Ireland {c |}{res}      3,454        0.19       96.05
{txt}                   Germany {c |}{res}     13,405        0.73       96.78
{txt}             Mediterranian {c |}{res}      5,286        0.29       97.07
{txt}                    Baltic {c |}{res}      4,957        0.27       97.34
{txt}               East Europe {c |}{res}      3,426        0.19       97.52
{txt}Hungary & Czech & Slovakia {c |}{res}      9,582        0.52       98.04
{txt} Benelux & Swizz & Austria {c |}{res}      6,362        0.35       98.39
{txt}               US & Canada {c |}{res}      4,219        0.23       98.62
{txt}           Central America {c |}{res}        554        0.03       98.65
{txt}                     Chile {c |}{res}      2,601        0.14       98.79
{txt}             South America {c |}{res}      2,925        0.16       98.95
{txt}               East Africa {c |}{res}      1,019        0.06       99.01
{txt}                      Mena {c |}{res}      3,015        0.16       99.17
{txt}              Other Africa {c |}{res}      1,588        0.09       99.26
{txt}                      Iran {c |}{res}      4,112        0.22       99.48
{txt}                      Iraq {c |}{res}      1,278        0.07       99.55
{txt}                    Turkey {c |}{res}      2,116        0.12       99.67
{txt}                 East Asia {c |}{res}      2,641        0.14       99.81
{txt}           South East Asia {c |}{res}      1,123        0.06       99.87
{txt}                South Asia {c |}{res}      2,058        0.11       99.98
{txt}                   Oceania {c |}{res}        296        0.02      100.00
{txt}      Unknown or Stateless {c |}{res}          7        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  1,836,861      100.00
{txt}(152,254 real changes made, 152,254 to missing)
(1,796,121 real changes made, 1,796,121 to missing)
(1,828,745 real changes made, 1,828,745 to missing)
(1,828,520 real changes made, 1,828,520 to missing)
(1,836,743 real changes made, 1,836,743 to missing)
(1,828,885 real changes made, 1,828,885 to missing)
(1,825,922 real changes made, 1,825,922 to missing)
(1,833,407 real changes made, 1,833,407 to missing)
(1,823,456 real changes made, 1,823,456 to missing)
(1,831,575 real changes made, 1,831,575 to missing)
(1,831,904 real changes made, 1,831,904 to missing)
(1,833,435 real changes made, 1,833,435 to missing)
(1,827,279 real changes made, 1,827,279 to missing)
(1,830,499 real changes made, 1,830,499 to missing)
(1,832,642 real changes made, 1,832,642 to missing)
(1,836,307 real changes made, 1,836,307 to missing)
(1,834,260 real changes made, 1,834,260 to missing)
(1,833,936 real changes made, 1,833,936 to missing)
(1,835,842 real changes made, 1,835,842 to missing)
(1,833,846 real changes made, 1,833,846 to missing)
(1,835,273 real changes made, 1,835,273 to missing)
(1,832,749 real changes made, 1,832,749 to missing)
(1,835,583 real changes made, 1,835,583 to missing)
(1,834,745 real changes made, 1,834,745 to missing)
(1,834,220 real changes made, 1,834,220 to missing)
(1,835,738 real changes made, 1,835,738 to missing)
(1,834,803 real changes made, 1,834,803 to missing)
(1,836,565 real changes made, 1,836,565 to missing)
(1,836,854 real changes made, 1,836,854 to missing)
file E:\ProjData\Ethnic Enclaves, political participation\temp\enclave_parish87.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,080,362 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(50 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,410,622
{txt}{col 9}from master{col 30}{res}           1,502{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,409,120{txt}  (_merge==2)

{col 5}matched{col 30}{res}          49,766{txt}  (_merge==3)
{col 5}{hline 41}
(1,502 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(30 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,613,791
{txt}{col 9}from master{col 30}{res}             336{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,613,455{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,458,520{txt}  (_merge==3)
{col 5}{hline 41}
(7,613,455 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,741,535       91.53       91.53
{txt}                   Finland {c |}{res}    222,519        2.63       94.16
{txt}          Norway & Iceland {c |}{res}     47,399        0.56       94.72
{txt}                   Denmark {c |}{res}     41,495        0.49       95.21
{txt}                Yugoslavia {c |}{res}     39,435        0.47       95.68
{txt}                   Germany {c |}{res}     37,656        0.45       96.12
{txt}                    Poland {c |}{res}     32,511        0.38       96.51
{txt}                      Iran {c |}{res}     28,936        0.34       96.85
{txt}             Mediterranian {c |}{res}     26,342        0.31       97.16
{txt}Hungary & Czech & Slovakia {c |}{res}     22,163        0.26       97.42
{txt}                    Turkey {c |}{res}     22,159        0.26       97.68
{txt}                      Mena {c |}{res}     22,020        0.26       97.94
{txt}                     Chile {c |}{res}     20,604        0.24       98.19
{txt} Benelux & Swizz & Austria {c |}{res}     16,412        0.19       98.38
{txt}                South Asia {c |}{res}     16,115        0.19       98.57
{txt}                    Baltic {c |}{res}     15,075        0.18       98.75
{txt}             South America {c |}{res}     14,632        0.17       98.92
{txt}               East Europe {c |}{res}     14,221        0.17       99.09
{txt}               US & Canada {c |}{res}     13,823        0.16       99.25
{txt}                 East Asia {c |}{res}     13,160        0.16       99.41
{txt}           South East Asia {c |}{res}     12,999        0.15       99.56
{txt}              UK & Ireland {c |}{res}     10,975        0.13       99.69
{txt}               East Africa {c |}{res}      7,144        0.08       99.78
{txt}              Other Africa {c |}{res}      6,652        0.08       99.86
{txt}                      Iraq {c |}{res}      6,410        0.08       99.93
{txt}           Central America {c |}{res}      3,463        0.04       99.97
{txt}                   Oceania {c |}{res}      1,493        0.02       99.99
{txt}                    Bosnia {c |}{res}        670        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         48        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,458,066      100.00

    {txt}Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,741,535       91.53       91.53
{txt}                   Finland {c |}{res}    222,519        2.63       94.16
{txt}                   Denmark {c |}{res}     41,495        0.49       94.65
{txt}          Norway & Iceland {c |}{res}     47,399        0.56       95.21
{txt}                    Bosnia {c |}{res}        670        0.01       95.22
{txt}                Yugoslavia {c |}{res}     39,435        0.47       95.68
{txt}                    Poland {c |}{res}     32,511        0.38       96.07
{txt}              UK & Ireland {c |}{res}     10,975        0.13       96.20
{txt}                   Germany {c |}{res}     37,656        0.45       96.64
{txt}             Mediterranian {c |}{res}     26,342        0.31       96.96
{txt}                    Baltic {c |}{res}     15,075        0.18       97.13
{txt}               East Europe {c |}{res}     14,221        0.17       97.30
{txt}Hungary & Czech & Slovakia {c |}{res}     22,163        0.26       97.56
{txt} Benelux & Swizz & Austria {c |}{res}     16,412        0.19       97.76
{txt}               US & Canada {c |}{res}     13,823        0.16       97.92
{txt}           Central America {c |}{res}      3,463        0.04       97.96
{txt}                     Chile {c |}{res}     20,604        0.24       98.21
{txt}             South America {c |}{res}     14,632        0.17       98.38
{txt}               East Africa {c |}{res}      7,144        0.08       98.46
{txt}                      Mena {c |}{res}     22,020        0.26       98.72
{txt}              Other Africa {c |}{res}      6,652        0.08       98.80
{txt}                      Iran {c |}{res}     28,936        0.34       99.14
{txt}                      Iraq {c |}{res}      6,410        0.08       99.22
{txt}                    Turkey {c |}{res}     22,159        0.26       99.48
{txt}                 East Asia {c |}{res}     13,160        0.16       99.64
{txt}           South East Asia {c |}{res}     12,999        0.15       99.79
{txt}                South Asia {c |}{res}     16,115        0.19       99.98
{txt}                   Oceania {c |}{res}      1,493        0.02      100.00
{txt}      Unknown or Stateless {c |}{res}         48        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,458,066      100.00
{txt}(716,531 real changes made, 716,531 to missing)
(8,235,547 real changes made, 8,235,547 to missing)
(8,416,571 real changes made, 8,416,571 to missing)
(8,410,667 real changes made, 8,410,667 to missing)
(8,457,396 real changes made, 8,457,396 to missing)
(8,418,631 real changes made, 8,418,631 to missing)
(8,425,555 real changes made, 8,425,555 to missing)
(8,447,091 real changes made, 8,447,091 to missing)
(8,420,410 real changes made, 8,420,410 to missing)
(8,431,724 real changes made, 8,431,724 to missing)
(8,442,991 real changes made, 8,442,991 to missing)
(8,443,845 real changes made, 8,443,845 to missing)
(8,435,903 real changes made, 8,435,903 to missing)
(8,441,654 real changes made, 8,441,654 to missing)
(8,444,243 real changes made, 8,444,243 to missing)
(8,454,603 real changes made, 8,454,603 to missing)
(8,437,462 real changes made, 8,437,462 to missing)
(8,443,434 real changes made, 8,443,434 to missing)
(8,450,922 real changes made, 8,450,922 to missing)
(8,436,046 real changes made, 8,436,046 to missing)
(8,451,414 real changes made, 8,451,414 to missing)
(8,429,130 real changes made, 8,429,130 to missing)
(8,451,656 real changes made, 8,451,656 to missing)
(8,435,907 real changes made, 8,435,907 to missing)
(8,444,906 real changes made, 8,444,906 to missing)
(8,445,067 real changes made, 8,445,067 to missing)
(8,441,951 real changes made, 8,441,951 to missing)
(8,456,573 real changes made, 8,456,573 to missing)
(8,458,018 real changes made, 8,458,018 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}      6,074       12.22       12.22
{txt}                   Finland {c |}{res}      4,676        9.41       21.62
{txt}                   Denmark {c |}{res}      2,067        4.16       25.78
{txt}          Norway & Iceland {c |}{res}      3,432        6.90       32.69
{txt}                    Bosnia {c |}{res}         21        0.04       32.73
{txt}                Yugoslavia {c |}{res}      1,110        2.23       34.96
{txt}                    Poland {c |}{res}      1,621        3.26       38.22
{txt}              UK & Ireland {c |}{res}        932        1.87       40.10
{txt}                   Germany {c |}{res}        637        1.28       41.38
{txt}             Mediterranian {c |}{res}        868        1.75       43.13
{txt}                    Baltic {c |}{res}         61        0.12       43.25
{txt}               East Europe {c |}{res}      1,760        3.54       46.79
{txt}Hungary & Czech & Slovakia {c |}{res}        714        1.44       48.22
{txt} Benelux & Swizz & Austria {c |}{res}        756        1.52       49.75
{txt}               US & Canada {c |}{res}        938        1.89       51.63
{txt}           Central America {c |}{res}        501        1.01       52.64
{txt}                     Chile {c |}{res}      2,916        5.87       58.51
{txt}             South America {c |}{res}        989        1.99       60.50
{txt}               East Africa {c |}{res}      1,626        3.27       63.77
{txt}                      Mena {c |}{res}      3,050        6.14       69.90
{txt}              Other Africa {c |}{res}        685        1.38       71.28
{txt}                      Iran {c |}{res}      8,013       16.12       87.40
{txt}                      Iraq {c |}{res}      1,249        2.51       89.91
{txt}                    Turkey {c |}{res}      1,235        2.48       92.40
{txt}                 East Asia {c |}{res}        987        1.99       94.38
{txt}           South East Asia {c |}{res}      1,449        2.91       97.30
{txt}                South Asia {c |}{res}      1,049        2.11       99.41
{txt}                   Oceania {c |}{res}        293        0.59      100.00
{txt}      Unknown or Stateless {c |}{res}          2        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     49,711      100.00
{txt}(43,637 real changes made, 43,637 to missing)
(45,035 real changes made, 45,035 to missing)
(47,644 real changes made, 47,644 to missing)
(46,279 real changes made, 46,279 to missing)
(49,690 real changes made, 49,690 to missing)
(48,601 real changes made, 48,601 to missing)
(48,090 real changes made, 48,090 to missing)
(48,779 real changes made, 48,779 to missing)
(49,074 real changes made, 49,074 to missing)
(48,843 real changes made, 48,843 to missing)
(49,650 real changes made, 49,650 to missing)
(47,951 real changes made, 47,951 to missing)
(48,997 real changes made, 48,997 to missing)
(48,955 real changes made, 48,955 to missing)
(48,773 real changes made, 48,773 to missing)
(49,210 real changes made, 49,210 to missing)
(46,795 real changes made, 46,795 to missing)
(48,722 real changes made, 48,722 to missing)
(48,085 real changes made, 48,085 to missing)
(46,661 real changes made, 46,661 to missing)
(49,026 real changes made, 49,026 to missing)
(41,698 real changes made, 41,698 to missing)
(48,462 real changes made, 48,462 to missing)
(48,476 real changes made, 48,476 to missing)
(48,724 real changes made, 48,724 to missing)
(48,262 real changes made, 48,262 to missing)
(48,662 real changes made, 48,662 to missing)
(49,418 real changes made, 49,418 to missing)
(49,709 real changes made, 49,709 to missing)
(7,741,535 missing values generated)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       3,689,002
{txt}{col 9}from master{col 30}{res}       3,084,740{txt}  (_merge==1)
{col 9}from using{col 30}{res}         604,262{txt}  (_merge==2)

{col 5}matched{col 30}{res}       5,374,118{txt}  (_merge==3)
{col 5}{hline 41}

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(2 observations deleted)
(3,807,017 missing values generated)
(3,192,983 missing values generated)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,496,153       93.50       93.50
{txt}                   Finland {c |}{res}    124,012        2.58       96.08
{txt}                   Denmark {c |}{res}     22,522        0.47       96.55
{txt}          Norway & Iceland {c |}{res}     23,817        0.50       97.05
{txt}                    Bosnia {c |}{res}        285        0.01       97.05
{txt}                Yugoslavia {c |}{res}     18,177        0.38       97.43
{txt}                    Poland {c |}{res}     11,333        0.24       97.67
{txt}              UK & Ireland {c |}{res}      3,306        0.07       97.74
{txt}                   Germany {c |}{res}     25,173        0.52       98.26
{txt}             Mediterranian {c |}{res}     13,364        0.28       98.54
{txt}                    Baltic {c |}{res}     12,356        0.26       98.79
{txt}               East Europe {c |}{res}      5,723        0.12       98.91
{txt}Hungary & Czech & Slovakia {c |}{res}     13,224        0.28       99.19
{txt} Benelux & Swizz & Austria {c |}{res}      8,872        0.18       99.37
{txt}               US & Canada {c |}{res}      6,511        0.14       99.51
{txt}           Central America {c |}{res}        416        0.01       99.52
{txt}                     Chile {c |}{res}      3,018        0.06       99.58
{txt}             South America {c |}{res}      2,777        0.06       99.64
{txt}               East Africa {c |}{res}        578        0.01       99.65
{txt}                      Mena {c |}{res}      3,973        0.08       99.73
{txt}              Other Africa {c |}{res}      1,600        0.03       99.76
{txt}                      Iran {c |}{res}        816        0.02       99.78
{txt}                      Iraq {c |}{res}        251        0.01       99.79
{txt}                    Turkey {c |}{res}      5,730        0.12       99.91
{txt}                 East Asia {c |}{res}      1,275        0.03       99.93
{txt}           South East Asia {c |}{res}      1,114        0.02       99.96
{txt}                South Asia {c |}{res}      1,867        0.04       99.99
{txt}                   Oceania {c |}{res}        244        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         18        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  4,808,505      100.00
{txt}(312,352 real changes made, 312,352 to missing)
(4,684,493 real changes made, 4,684,493 to missing)
(4,785,983 real changes made, 4,785,983 to missing)
(4,784,688 real changes made, 4,784,688 to missing)
(4,808,220 real changes made, 4,808,220 to missing)
(4,790,328 real changes made, 4,790,328 to missing)
(4,797,172 real changes made, 4,797,172 to missing)
(4,805,199 real changes made, 4,805,199 to missing)
(4,783,332 real changes made, 4,783,332 to missing)
(4,795,141 real changes made, 4,795,141 to missing)
(4,796,149 real changes made, 4,796,149 to missing)
(4,802,782 real changes made, 4,802,782 to missing)
(4,795,281 real changes made, 4,795,281 to missing)
(4,799,633 real changes made, 4,799,633 to missing)
(4,801,994 real changes made, 4,801,994 to missing)
(4,808,089 real changes made, 4,808,089 to missing)
(4,805,487 real changes made, 4,805,487 to missing)
(4,805,728 real changes made, 4,805,728 to missing)
(4,807,927 real changes made, 4,807,927 to missing)
(4,804,532 real changes made, 4,804,532 to missing)
(4,806,905 real changes made, 4,806,905 to missing)
(4,807,689 real changes made, 4,807,689 to missing)
(4,808,254 real changes made, 4,808,254 to missing)
(4,802,775 real changes made, 4,802,775 to missing)
(4,807,230 real changes made, 4,807,230 to missing)
(4,807,391 real changes made, 4,807,391 to missing)
(4,806,638 real changes made, 4,806,638 to missing)
(4,808,261 real changes made, 4,808,261 to missing)
(4,808,487 real changes made, 4,808,487 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,846,083       92.01       92.01
{txt}                   Finland {c |}{res}    172,657        3.28       95.29
{txt}                   Denmark {c |}{res}     30,204        0.57       95.86
{txt}          Norway & Iceland {c |}{res}     30,844        0.59       96.45
{txt}                    Bosnia {c |}{res}        481        0.01       96.46
{txt}                Yugoslavia {c |}{res}     29,403        0.56       97.01
{txt}                    Poland {c |}{res}     14,954        0.28       97.30
{txt}              UK & Ireland {c |}{res}      4,976        0.09       97.39
{txt}                   Germany {c |}{res}     29,382        0.56       97.95
{txt}             Mediterranian {c |}{res}     18,367        0.35       98.30
{txt}                    Baltic {c |}{res}     13,665        0.26       98.56
{txt}               East Europe {c |}{res}      6,885        0.13       98.69
{txt}Hungary & Czech & Slovakia {c |}{res}     15,947        0.30       98.99
{txt} Benelux & Swizz & Austria {c |}{res}     11,010        0.21       99.20
{txt}               US & Canada {c |}{res}      7,822        0.15       99.35
{txt}           Central America {c |}{res}        612        0.01       99.36
{txt}                     Chile {c |}{res}      3,652        0.07       99.43
{txt}             South America {c |}{res}      3,602        0.07       99.50
{txt}               East Africa {c |}{res}        937        0.02       99.52
{txt}                      Mena {c |}{res}      6,024        0.11       99.63
{txt}              Other Africa {c |}{res}      2,185        0.04       99.67
{txt}                      Iran {c |}{res}      1,546        0.03       99.70
{txt}                      Iraq {c |}{res}        386        0.01       99.71
{txt}                    Turkey {c |}{res}      8,547        0.16       99.87
{txt}                 East Asia {c |}{res}      2,125        0.04       99.91
{txt}           South East Asia {c |}{res}      1,666        0.03       99.94
{txt}                South Asia {c |}{res}      2,584        0.05       99.99
{txt}                   Oceania {c |}{res}        326        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         32        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  5,266,904      100.00
{txt}(420,821 real changes made, 420,821 to missing)
(5,094,247 real changes made, 5,094,247 to missing)
(5,236,700 real changes made, 5,236,700 to missing)
(5,236,060 real changes made, 5,236,060 to missing)
(5,266,423 real changes made, 5,266,423 to missing)
(5,237,501 real changes made, 5,237,501 to missing)
(5,251,950 real changes made, 5,251,950 to missing)
(5,261,928 real changes made, 5,261,928 to missing)
(5,237,522 real changes made, 5,237,522 to missing)
(5,248,537 real changes made, 5,248,537 to missing)
(5,253,239 real changes made, 5,253,239 to missing)
(5,260,019 real changes made, 5,260,019 to missing)
(5,250,957 real changes made, 5,250,957 to missing)
(5,255,894 real changes made, 5,255,894 to missing)
(5,259,082 real changes made, 5,259,082 to missing)
(5,266,292 real changes made, 5,266,292 to missing)
(5,263,252 real changes made, 5,263,252 to missing)
(5,263,302 real changes made, 5,263,302 to missing)
(5,265,967 real changes made, 5,265,967 to missing)
(5,260,880 real changes made, 5,260,880 to missing)
(5,264,719 real changes made, 5,264,719 to missing)
(5,265,358 real changes made, 5,265,358 to missing)
(5,266,518 real changes made, 5,266,518 to missing)
(5,258,357 real changes made, 5,258,357 to missing)
(5,264,779 real changes made, 5,264,779 to missing)
(5,265,238 real changes made, 5,265,238 to missing)
(5,264,320 real changes made, 5,264,320 to missing)
(5,266,578 real changes made, 5,266,578 to missing)
(5,266,872 real changes made, 5,266,872 to missing)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}         815,620
{txt}{col 9}from master{col 30}{res}         762,078{txt}  (_merge==1)
{col 9}from using{col 30}{res}          53,542{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,301,040{txt}  (_merge==3)
{col 5}{hline 41}
(53,542 observations deleted)
(5,994,593 missing values generated)
(8,937,438 missing values generated)
(9,043,848 missing values generated)
(9,045,514 missing values generated)
(9,062,768 missing values generated)
(9,042,783 missing values generated)
(9,051,310 missing values generated)
(9,058,551 missing values generated)
(9,043,533 missing values generated)
(9,050,390 missing values generated)
(9,056,906 missing values generated)
(9,059,265 missing values generated)
(9,052,136 missing values generated)
(9,055,255 missing values generated)
(9,058,712 missing values generated)
(9,062,587 missing values generated)
(9,058,866 missing values generated)
(9,059,833 missing values generated)
(9,062,048 missing values generated)
(9,058,039 missing values generated)
(9,061,320 missing values generated)
(9,061,520 missing values generated)
(9,062,173 missing values generated)
(9,056,331 missing values generated)
(9,061,020 missing values generated)
(9,060,355 missing values generated)
(9,060,562 missing values generated)
(9,062,786 missing values generated)
(9,063,104 missing values generated)
(1,664,471 missing values generated)
(8,850,887 missing values generated)
(9,025,134 missing values generated)
(9,022,516 missing values generated)
(9,062,507 missing values generated)
(9,026,675 missing values generated)
(9,035,637 missing values generated)
(9,054,296 missing values generated)
(9,027,221 missing values generated)
(9,039,104 missing values generated)
(9,048,185 missing values generated)
(9,052,898 missing values generated)
(9,042,688 missing values generated)
(9,048,450 missing values generated)
(9,051,739 missing values generated)
(9,060,861 missing values generated)
(9,050,309 missing values generated)
(9,051,294 missing values generated)
(9,059,612 missing values generated)
(9,048,773 missing values generated)
(9,058,272 missing values generated)
(9,054,769 missing values generated)
(9,059,177 missing values generated)
(9,044,658 missing values generated)
(9,052,652 missing values generated)
(9,053,305 missing values generated)
(9,050,401 missing values generated)
(9,062,190 missing values generated)
(9,063,075 missing values generated)
(4,491,411 missing values generated)
(2,733,023 real changes made, 2,733,023 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  1,684,552       91.71       91.71
{txt}                   Finland {c |}{res}     40,757        2.22       93.93
{txt}                   Denmark {c |}{res}      8,129        0.44       94.37
{txt}          Norway & Iceland {c |}{res}      8,337        0.45       94.82
{txt}                    Bosnia {c |}{res}        119        0.01       94.83
{txt}                Yugoslavia {c |}{res}      7,985        0.43       95.26
{txt}                    Poland {c |}{res}     10,943        0.60       95.86
{txt}              UK & Ireland {c |}{res}      3,452        0.19       96.05
{txt}                   Germany {c |}{res}     13,411        0.73       96.78
{txt}             Mediterranian {c |}{res}      5,286        0.29       97.06
{txt}                    Baltic {c |}{res}      4,954        0.27       97.33
{txt}               East Europe {c |}{res}      3,426        0.19       97.52
{txt}Hungary & Czech & Slovakia {c |}{res}      9,583        0.52       98.04
{txt} Benelux & Swizz & Austria {c |}{res}      6,368        0.35       98.39
{txt}               US & Canada {c |}{res}      4,218        0.23       98.62
{txt}           Central America {c |}{res}        554        0.03       98.65
{txt}                     Chile {c |}{res}      2,607        0.14       98.79
{txt}             South America {c |}{res}      2,932        0.16       98.95
{txt}               East Africa {c |}{res}      1,025        0.06       99.01
{txt}                      Mena {c |}{res}      3,016        0.16       99.17
{txt}              Other Africa {c |}{res}      1,590        0.09       99.26
{txt}                      Iran {c |}{res}      4,115        0.22       99.48
{txt}                      Iraq {c |}{res}      1,278        0.07       99.55
{txt}                    Turkey {c |}{res}      2,119        0.12       99.67
{txt}                 East Asia {c |}{res}      2,641        0.14       99.81
{txt}           South East Asia {c |}{res}      1,126        0.06       99.87
{txt}                South Asia {c |}{res}      2,059        0.11       99.98
{txt}                   Oceania {c |}{res}        297        0.02      100.00
{txt}      Unknown or Stateless {c |}{res}          7        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  1,836,886      100.00
{txt}(152,334 real changes made, 152,334 to missing)
(1,796,129 real changes made, 1,796,129 to missing)
(1,828,757 real changes made, 1,828,757 to missing)
(1,828,549 real changes made, 1,828,549 to missing)
(1,836,767 real changes made, 1,836,767 to missing)
(1,828,901 real changes made, 1,828,901 to missing)
(1,825,943 real changes made, 1,825,943 to missing)
(1,833,434 real changes made, 1,833,434 to missing)
(1,823,475 real changes made, 1,823,475 to missing)
(1,831,600 real changes made, 1,831,600 to missing)
(1,831,932 real changes made, 1,831,932 to missing)
(1,833,460 real changes made, 1,833,460 to missing)
(1,827,303 real changes made, 1,827,303 to missing)
(1,830,518 real changes made, 1,830,518 to missing)
(1,832,668 real changes made, 1,832,668 to missing)
(1,836,332 real changes made, 1,836,332 to missing)
(1,834,279 real changes made, 1,834,279 to missing)
(1,833,954 real changes made, 1,833,954 to missing)
(1,835,861 real changes made, 1,835,861 to missing)
(1,833,870 real changes made, 1,833,870 to missing)
(1,835,296 real changes made, 1,835,296 to missing)
(1,832,771 real changes made, 1,832,771 to missing)
(1,835,608 real changes made, 1,835,608 to missing)
(1,834,767 real changes made, 1,834,767 to missing)
(1,834,245 real changes made, 1,834,245 to missing)
(1,835,760 real changes made, 1,835,760 to missing)
(1,834,827 real changes made, 1,834,827 to missing)
(1,836,589 real changes made, 1,836,589 to missing)
(1,836,879 real changes made, 1,836,879 to missing)
file E:\ProjData\Ethnic Enclaves, political participation\temp\enclave_parish88.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,065,671 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(76 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,466,017
{txt}{col 9}from master{col 30}{res}           2,459{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,463,558{txt}  (_merge==2)

{col 5}matched{col 30}{res}          63,480{txt}  (_merge==3)
{col 5}{hline 41}
(2,459 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(62 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,545,835
{txt}{col 9}from master{col 30}{res}             418{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,545,417{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,526,558{txt}  (_merge==3)
{col 5}{hline 41}
(7,545,417 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,768,810       91.12       91.12
{txt}                   Finland {c |}{res}    220,238        2.58       93.70
{txt}          Norway & Iceland {c |}{res}     54,158        0.64       94.34
{txt}                   Denmark {c |}{res}     43,647        0.51       94.85
{txt}                Yugoslavia {c |}{res}     40,685        0.48       95.33
{txt}                   Germany {c |}{res}     37,688        0.44       95.77
{txt}                      Iran {c |}{res}     35,561        0.42       96.18
{txt}                    Poland {c |}{res}     33,932        0.40       96.58
{txt}                      Mena {c |}{res}     27,916        0.33       96.91
{txt}             Mediterranian {c |}{res}     26,589        0.31       97.22
{txt}                     Chile {c |}{res}     26,248        0.31       97.53
{txt}                    Turkey {c |}{res}     23,636        0.28       97.81
{txt}Hungary & Czech & Slovakia {c |}{res}     22,945        0.27       98.08
{txt}                South Asia {c |}{res}     17,177        0.20       98.28
{txt} Benelux & Swizz & Austria {c |}{res}     16,507        0.19       98.47
{txt}               East Europe {c |}{res}     15,961        0.19       98.66
{txt}             South America {c |}{res}     15,517        0.18       98.84
{txt}                    Baltic {c |}{res}     14,677        0.17       99.01
{txt}           South East Asia {c |}{res}     14,343        0.17       99.18
{txt}               US & Canada {c |}{res}     14,232        0.17       99.35
{txt}                 East Asia {c |}{res}     13,876        0.16       99.51
{txt}              UK & Ireland {c |}{res}     11,387        0.13       99.64
{txt}               East Africa {c |}{res}      9,141        0.11       99.75
{txt}                      Iraq {c |}{res}      7,623        0.09       99.84
{txt}              Other Africa {c |}{res}      7,330        0.09       99.93
{txt}           Central America {c |}{res}      3,865        0.05       99.97
{txt}                   Oceania {c |}{res}      1,656        0.02       99.99
{txt}                    Bosnia {c |}{res}        715        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         48        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,526,108      100.00

    {txt}Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,768,810       91.12       91.12
{txt}                   Finland {c |}{res}    220,238        2.58       93.70
{txt}                   Denmark {c |}{res}     43,647        0.51       94.21
{txt}          Norway & Iceland {c |}{res}     54,158        0.64       94.85
{txt}                    Bosnia {c |}{res}        715        0.01       94.86
{txt}                Yugoslavia {c |}{res}     40,685        0.48       95.33
{txt}                    Poland {c |}{res}     33,932        0.40       95.73
{txt}              UK & Ireland {c |}{res}     11,387        0.13       95.87
{txt}                   Germany {c |}{res}     37,688        0.44       96.31
{txt}             Mediterranian {c |}{res}     26,589        0.31       96.62
{txt}                    Baltic {c |}{res}     14,677        0.17       96.79
{txt}               East Europe {c |}{res}     15,961        0.19       96.98
{txt}Hungary & Czech & Slovakia {c |}{res}     22,945        0.27       97.25
{txt} Benelux & Swizz & Austria {c |}{res}     16,507        0.19       97.44
{txt}               US & Canada {c |}{res}     14,232        0.17       97.61
{txt}           Central America {c |}{res}      3,865        0.05       97.65
{txt}                     Chile {c |}{res}     26,248        0.31       97.96
{txt}             South America {c |}{res}     15,517        0.18       98.14
{txt}               East Africa {c |}{res}      9,141        0.11       98.25
{txt}                      Mena {c |}{res}     27,916        0.33       98.58
{txt}              Other Africa {c |}{res}      7,330        0.09       98.66
{txt}                      Iran {c |}{res}     35,561        0.42       99.08
{txt}                      Iraq {c |}{res}      7,623        0.09       99.17
{txt}                    Turkey {c |}{res}     23,636        0.28       99.45
{txt}                 East Asia {c |}{res}     13,876        0.16       99.61
{txt}           South East Asia {c |}{res}     14,343        0.17       99.78
{txt}                South Asia {c |}{res}     17,177        0.20       99.98
{txt}                   Oceania {c |}{res}      1,656        0.02      100.00
{txt}      Unknown or Stateless {c |}{res}         48        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,526,108      100.00
{txt}(757,298 real changes made, 757,298 to missing)
(8,305,870 real changes made, 8,305,870 to missing)
(8,482,461 real changes made, 8,482,461 to missing)
(8,471,950 real changes made, 8,471,950 to missing)
(8,525,393 real changes made, 8,525,393 to missing)
(8,485,423 real changes made, 8,485,423 to missing)
(8,492,176 real changes made, 8,492,176 to missing)
(8,514,721 real changes made, 8,514,721 to missing)
(8,488,420 real changes made, 8,488,420 to missing)
(8,499,519 real changes made, 8,499,519 to missing)
(8,511,431 real changes made, 8,511,431 to missing)
(8,510,147 real changes made, 8,510,147 to missing)
(8,503,163 real changes made, 8,503,163 to missing)
(8,509,601 real changes made, 8,509,601 to missing)
(8,511,876 real changes made, 8,511,876 to missing)
(8,522,243 real changes made, 8,522,243 to missing)
(8,499,860 real changes made, 8,499,860 to missing)
(8,510,591 real changes made, 8,510,591 to missing)
(8,516,967 real changes made, 8,516,967 to missing)
(8,498,192 real changes made, 8,498,192 to missing)
(8,518,778 real changes made, 8,518,778 to missing)
(8,490,547 real changes made, 8,490,547 to missing)
(8,518,485 real changes made, 8,518,485 to missing)
(8,502,472 real changes made, 8,502,472 to missing)
(8,512,232 real changes made, 8,512,232 to missing)
(8,511,765 real changes made, 8,511,765 to missing)
(8,508,931 real changes made, 8,508,931 to missing)
(8,524,452 real changes made, 8,524,452 to missing)
(8,526,060 real changes made, 8,526,060 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}      6,262        9.88        9.88
{txt}                   Finland {c |}{res}      3,941        6.22       16.09
{txt}                   Denmark {c |}{res}      3,638        5.74       21.83
{txt}          Norway & Iceland {c |}{res}      8,932       14.09       35.91
{txt}                    Bosnia {c |}{res}         47        0.07       35.99
{txt}                Yugoslavia {c |}{res}      1,598        2.52       38.51
{txt}                    Poland {c |}{res}      1,755        2.77       41.28
{txt}              UK & Ireland {c |}{res}        835        1.32       42.59
{txt}                   Germany {c |}{res}        770        1.21       43.81
{txt}             Mediterranian {c |}{res}        895        1.41       45.22
{txt}                    Baltic {c |}{res}         57        0.09       45.31
{txt}               East Europe {c |}{res}      2,017        3.18       48.49
{txt}Hungary & Czech & Slovakia {c |}{res}      1,086        1.71       50.20
{txt} Benelux & Swizz & Austria {c |}{res}        618        0.97       51.18
{txt}               US & Canada {c |}{res}        995        1.57       52.75
{txt}           Central America {c |}{res}        470        0.74       53.49
{txt}                     Chile {c |}{res}      5,837        9.21       62.69
{txt}             South America {c |}{res}      1,142        1.80       64.49
{txt}               East Africa {c |}{res}      2,051        3.23       67.73
{txt}                      Mena {c |}{res}      6,129        9.67       77.39
{txt}              Other Africa {c |}{res}        789        1.24       78.64
{txt}                      Iran {c |}{res}      6,819       10.75       89.39
{txt}                      Iraq {c |}{res}      1,263        1.99       91.38
{txt}                    Turkey {c |}{res}      1,645        2.59       93.98
{txt}                 East Asia {c |}{res}        925        1.46       95.44
{txt}           South East Asia {c |}{res}      1,450        2.29       97.72
{txt}                South Asia {c |}{res}      1,175        1.85       99.58
{txt}                   Oceania {c |}{res}        264        0.42       99.99
{txt}      Unknown or Stateless {c |}{res}          4        0.01      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     63,409      100.00
{txt}(57,147 real changes made, 57,147 to missing)
(59,468 real changes made, 59,468 to missing)
(59,771 real changes made, 59,771 to missing)
(54,477 real changes made, 54,477 to missing)
(63,362 real changes made, 63,362 to missing)
(61,811 real changes made, 61,811 to missing)
(61,654 real changes made, 61,654 to missing)
(62,574 real changes made, 62,574 to missing)
(62,639 real changes made, 62,639 to missing)
(62,514 real changes made, 62,514 to missing)
(63,352 real changes made, 63,352 to missing)
(61,392 real changes made, 61,392 to missing)
(62,323 real changes made, 62,323 to missing)
(62,791 real changes made, 62,791 to missing)
(62,414 real changes made, 62,414 to missing)
(62,939 real changes made, 62,939 to missing)
(57,572 real changes made, 57,572 to missing)
(62,267 real changes made, 62,267 to missing)
(61,358 real changes made, 61,358 to missing)
(57,280 real changes made, 57,280 to missing)
(62,620 real changes made, 62,620 to missing)
(56,590 real changes made, 56,590 to missing)
(62,146 real changes made, 62,146 to missing)
(61,764 real changes made, 61,764 to missing)
(62,484 real changes made, 62,484 to missing)
(61,959 real changes made, 61,959 to missing)
(62,234 real changes made, 62,234 to missing)
(63,145 real changes made, 63,145 to missing)
(63,405 real changes made, 63,405 to missing)
(7,768,810 missing values generated)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       3,929,358
{txt}{col 9}from master{col 30}{res}       3,238,978{txt}  (_merge==1)
{col 9}from using{col 30}{res}         690,380{txt}  (_merge==2)

{col 5}matched{col 30}{res}       5,288,000{txt}  (_merge==3)
{col 5}{hline 41}

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(2 observations deleted)
(3,961,255 missing values generated)
(3,347,221 missing values generated)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,426,595       93.49       93.49
{txt}                   Finland {c |}{res}    122,387        2.58       96.08
{txt}                   Denmark {c |}{res}     22,196        0.47       96.55
{txt}          Norway & Iceland {c |}{res}     23,395        0.49       97.04
{txt}                    Bosnia {c |}{res}        285        0.01       97.05
{txt}                Yugoslavia {c |}{res}     18,091        0.38       97.43
{txt}                    Poland {c |}{res}     11,196        0.24       97.67
{txt}              UK & Ireland {c |}{res}      3,258        0.07       97.74
{txt}                   Germany {c |}{res}     24,812        0.52       98.26
{txt}             Mediterranian {c |}{res}     13,160        0.28       98.54
{txt}                    Baltic {c |}{res}     12,018        0.25       98.79
{txt}               East Europe {c |}{res}      5,583        0.12       98.91
{txt}Hungary & Czech & Slovakia {c |}{res}     13,055        0.28       99.18
{txt} Benelux & Swizz & Austria {c |}{res}      8,727        0.18       99.37
{txt}               US & Canada {c |}{res}      6,361        0.13       99.50
{txt}           Central America {c |}{res}        410        0.01       99.51
{txt}                     Chile {c |}{res}      2,997        0.06       99.58
{txt}             South America {c |}{res}      2,740        0.06       99.63
{txt}               East Africa {c |}{res}        574        0.01       99.65
{txt}                      Mena {c |}{res}      3,963        0.08       99.73
{txt}              Other Africa {c |}{res}      1,583        0.03       99.76
{txt}                      Iran {c |}{res}        813        0.02       99.78
{txt}                      Iraq {c |}{res}        252        0.01       99.79
{txt}                    Turkey {c |}{res}      5,680        0.12       99.91
{txt}                 East Asia {c |}{res}      1,263        0.03       99.93
{txt}           South East Asia {c |}{res}      1,105        0.02       99.96
{txt}                South Asia {c |}{res}      1,862        0.04       99.99
{txt}                   Oceania {c |}{res}        242        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         17        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  4,734,620      100.00
{txt}(308,025 real changes made, 308,025 to missing)
(4,612,233 real changes made, 4,612,233 to missing)
(4,712,424 real changes made, 4,712,424 to missing)
(4,711,225 real changes made, 4,711,225 to missing)
(4,734,335 real changes made, 4,734,335 to missing)
(4,716,529 real changes made, 4,716,529 to missing)
(4,723,424 real changes made, 4,723,424 to missing)
(4,731,362 real changes made, 4,731,362 to missing)
(4,709,808 real changes made, 4,709,808 to missing)
(4,721,460 real changes made, 4,721,460 to missing)
(4,722,602 real changes made, 4,722,602 to missing)
(4,729,037 real changes made, 4,729,037 to missing)
(4,721,565 real changes made, 4,721,565 to missing)
(4,725,893 real changes made, 4,725,893 to missing)
(4,728,259 real changes made, 4,728,259 to missing)
(4,734,210 real changes made, 4,734,210 to missing)
(4,731,623 real changes made, 4,731,623 to missing)
(4,731,880 real changes made, 4,731,880 to missing)
(4,734,046 real changes made, 4,734,046 to missing)
(4,730,657 real changes made, 4,730,657 to missing)
(4,733,037 real changes made, 4,733,037 to missing)
(4,733,807 real changes made, 4,733,807 to missing)
(4,734,368 real changes made, 4,734,368 to missing)
(4,728,940 real changes made, 4,728,940 to missing)
(4,733,357 real changes made, 4,733,357 to missing)
(4,733,515 real changes made, 4,733,515 to missing)
(4,732,758 real changes made, 4,732,758 to missing)
(4,734,378 real changes made, 4,734,378 to missing)
(4,734,603 real changes made, 4,734,603 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,766,375       92.00       92.00
{txt}                   Finland {c |}{res}    170,035        3.28       95.28
{txt}                   Denmark {c |}{res}     29,720        0.57       95.85
{txt}          Norway & Iceland {c |}{res}     30,265        0.58       96.44
{txt}                    Bosnia {c |}{res}        481        0.01       96.45
{txt}                Yugoslavia {c |}{res}     29,241        0.56       97.01
{txt}                    Poland {c |}{res}     14,783        0.29       97.30
{txt}              UK & Ireland {c |}{res}      4,905        0.09       97.39
{txt}                   Germany {c |}{res}     28,923        0.56       97.95
{txt}             Mediterranian {c |}{res}     18,084        0.35       98.30
{txt}                    Baltic {c |}{res}     13,255        0.26       98.55
{txt}               East Europe {c |}{res}      6,711        0.13       98.68
{txt}Hungary & Czech & Slovakia {c |}{res}     15,760        0.30       98.99
{txt} Benelux & Swizz & Austria {c |}{res}     10,836        0.21       99.20
{txt}               US & Canada {c |}{res}      7,625        0.15       99.34
{txt}           Central America {c |}{res}        605        0.01       99.36
{txt}                     Chile {c |}{res}      3,622        0.07       99.43
{txt}             South America {c |}{res}      3,554        0.07       99.49
{txt}               East Africa {c |}{res}        933        0.02       99.51
{txt}                      Mena {c |}{res}      5,996        0.12       99.63
{txt}              Other Africa {c |}{res}      2,161        0.04       99.67
{txt}                      Iran {c |}{res}      1,536        0.03       99.70
{txt}                      Iraq {c |}{res}        388        0.01       99.71
{txt}                    Turkey {c |}{res}      8,485        0.16       99.87
{txt}                 East Asia {c |}{res}      2,103        0.04       99.91
{txt}           South East Asia {c |}{res}      1,651        0.03       99.94
{txt}                South Asia {c |}{res}      2,576        0.05       99.99
{txt}                   Oceania {c |}{res}        326        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         30        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  5,180,965      100.00
{txt}(414,590 real changes made, 414,590 to missing)
(5,010,930 real changes made, 5,010,930 to missing)
(5,151,245 real changes made, 5,151,245 to missing)
(5,150,700 real changes made, 5,150,700 to missing)
(5,180,484 real changes made, 5,180,484 to missing)
(5,151,724 real changes made, 5,151,724 to missing)
(5,166,182 real changes made, 5,166,182 to missing)
(5,176,060 real changes made, 5,176,060 to missing)
(5,152,042 real changes made, 5,152,042 to missing)
(5,162,881 real changes made, 5,162,881 to missing)
(5,167,710 real changes made, 5,167,710 to missing)
(5,174,254 real changes made, 5,174,254 to missing)
(5,165,205 real changes made, 5,165,205 to missing)
(5,170,129 real changes made, 5,170,129 to missing)
(5,173,340 real changes made, 5,173,340 to missing)
(5,180,360 real changes made, 5,180,360 to missing)
(5,177,343 real changes made, 5,177,343 to missing)
(5,177,411 real changes made, 5,177,411 to missing)
(5,180,032 real changes made, 5,180,032 to missing)
(5,174,969 real changes made, 5,174,969 to missing)
(5,178,804 real changes made, 5,178,804 to missing)
(5,179,429 real changes made, 5,179,429 to missing)
(5,180,577 real changes made, 5,180,577 to missing)
(5,172,480 real changes made, 5,172,480 to missing)
(5,178,862 real changes made, 5,178,862 to missing)
(5,179,314 real changes made, 5,179,314 to missing)
(5,178,389 real changes made, 5,178,389 to missing)
(5,180,639 real changes made, 5,180,639 to missing)
(5,180,935 real changes made, 5,180,935 to missing)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}         993,926
{txt}{col 9}from master{col 30}{res}         928,350{txt}  (_merge==1)
{col 9}from using{col 30}{res}          65,576{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,289,006{txt}  (_merge==3)
{col 5}{hline 41}
(65,576 observations deleted)
(6,158,738 missing values generated)
(9,093,183 missing values generated)
(9,198,251 missing values generated)
(9,199,914 missing values generated)
(9,217,006 missing values generated)
(9,197,078 missing values generated)
(9,205,587 missing values generated)
(9,212,832 missing values generated)
(9,197,894 missing values generated)
(9,204,791 missing values generated)
(9,211,191 missing values generated)
(9,213,531 missing values generated)
(9,206,418 missing values generated)
(9,209,583 missing values generated)
(9,212,993 missing values generated)
(9,216,829 missing values generated)
(9,213,125 missing values generated)
(9,214,110 missing values generated)
(9,216,289 missing values generated)
(9,212,301 missing values generated)
(9,215,575 missing values generated)
(9,215,764 missing values generated)
(9,216,415 missing values generated)
(9,210,597 missing values generated)
(9,215,276 missing values generated)
(9,214,610 missing values generated)
(9,214,815 missing values generated)
(9,217,030 missing values generated)
(9,217,343 missing values generated)
(1,908,136 missing values generated)
(9,008,827 missing values generated)
(9,180,019 missing values generated)
(9,177,693 missing values generated)
(9,216,745 missing values generated)
(9,181,152 missing values generated)
(9,190,142 missing values generated)
(9,208,708 missing values generated)
(9,182,037 missing values generated)
(9,193,777 missing values generated)
(9,202,870 missing values generated)
(9,207,373 missing values generated)
(9,197,163 missing values generated)
(9,202,980 missing values generated)
(9,206,287 missing values generated)
(9,215,126 missing values generated)
(9,204,643 missing values generated)
(9,205,681 missing values generated)
(9,213,862 missing values generated)
(9,203,102 missing values generated)
(9,212,565 missing values generated)
(9,209,066 missing values generated)
(9,213,429 missing values generated)
(9,199,021 missing values generated)
(9,206,964 missing values generated)
(9,207,605 missing values generated)
(9,204,678 missing values generated)
(9,216,444 missing values generated)
(9,217,316 missing values generated)
(4,491,882 missing values generated)
(2,886,536 real changes made, 2,886,536 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  1,685,276       91.70       91.70
{txt}                   Finland {c |}{res}     40,817        2.22       93.92
{txt}                   Denmark {c |}{res}      8,142        0.44       94.36
{txt}          Norway & Iceland {c |}{res}      8,389        0.46       94.82
{txt}                    Bosnia {c |}{res}        119        0.01       94.82
{txt}                Yugoslavia {c |}{res}      7,993        0.43       95.26
{txt}                    Poland {c |}{res}     10,953        0.60       95.85
{txt}              UK & Ireland {c |}{res}      3,462        0.19       96.04
{txt}                   Germany {c |}{res}     13,419        0.73       96.77
{txt}             Mediterranian {c |}{res}      5,301        0.29       97.06
{txt}                    Baltic {c |}{res}      4,957        0.27       97.33
{txt}               East Europe {c |}{res}      3,428        0.19       97.52
{txt}Hungary & Czech & Slovakia {c |}{res}      9,591        0.52       98.04
{txt} Benelux & Swizz & Austria {c |}{res}      6,378        0.35       98.39
{txt}               US & Canada {c |}{res}      4,238        0.23       98.62
{txt}           Central America {c |}{res}        552        0.03       98.65
{txt}                     Chile {c |}{res}      2,608        0.14       98.79
{txt}             South America {c |}{res}      2,938        0.16       98.95
{txt}               East Africa {c |}{res}      1,026        0.06       99.01
{txt}                      Mena {c |}{res}      3,025        0.16       99.17
{txt}              Other Africa {c |}{res}      1,594        0.09       99.26
{txt}                      Iran {c |}{res}      4,117        0.22       99.48
{txt}                      Iraq {c |}{res}      1,279        0.07       99.55
{txt}                    Turkey {c |}{res}      2,122        0.12       99.67
{txt}                 East Asia {c |}{res}      2,646        0.14       99.81
{txt}           South East Asia {c |}{res}      1,131        0.06       99.87
{txt}                South Asia {c |}{res}      2,066        0.11       99.98
{txt}                   Oceania {c |}{res}        298        0.02      100.00
{txt}      Unknown or Stateless {c |}{res}          7        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  1,837,872      100.00
{txt}(152,596 real changes made, 152,596 to missing)
(1,797,055 real changes made, 1,797,055 to missing)
(1,829,730 real changes made, 1,829,730 to missing)
(1,829,483 real changes made, 1,829,483 to missing)
(1,837,753 real changes made, 1,837,753 to missing)
(1,829,879 real changes made, 1,829,879 to missing)
(1,826,919 real changes made, 1,826,919 to missing)
(1,834,410 real changes made, 1,834,410 to missing)
(1,824,453 real changes made, 1,824,453 to missing)
(1,832,571 real changes made, 1,832,571 to missing)
(1,832,915 real changes made, 1,832,915 to missing)
(1,834,444 real changes made, 1,834,444 to missing)
(1,828,281 real changes made, 1,828,281 to missing)
(1,831,494 real changes made, 1,831,494 to missing)
(1,833,634 real changes made, 1,833,634 to missing)
(1,837,320 real changes made, 1,837,320 to missing)
(1,835,264 real changes made, 1,835,264 to missing)
(1,834,934 real changes made, 1,834,934 to missing)
(1,836,846 real changes made, 1,836,846 to missing)
(1,834,847 real changes made, 1,834,847 to missing)
(1,836,278 real changes made, 1,836,278 to missing)
(1,833,755 real changes made, 1,833,755 to missing)
(1,836,593 real changes made, 1,836,593 to missing)
(1,835,750 real changes made, 1,835,750 to missing)
(1,835,226 real changes made, 1,835,226 to missing)
(1,836,741 real changes made, 1,836,741 to missing)
(1,835,806 real changes made, 1,835,806 to missing)
(1,837,574 real changes made, 1,837,574 to missing)
(1,837,865 real changes made, 1,837,865 to missing)
file E:\ProjData\Ethnic Enclaves, political participation\temp\enclave_parish89.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,071,534 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(62 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,535,856
{txt}{col 9}from master{col 30}{res}           2,661{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,533,195{txt}  (_merge==2)

{col 5}matched{col 30}{res}          57,431{txt}  (_merge==3)
{col 5}{hline 41}
(2,661 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(90 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,482,397
{txt}{col 9}from master{col 30}{res}             479{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,481,918{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,590,057{txt}  (_merge==3)
{col 5}{hline 41}
(7,481,918 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,800,378       90.81       90.81
{txt}                   Finland {c |}{res}    217,395        2.53       93.34
{txt}          Norway & Iceland {c |}{res}     57,470        0.67       94.01
{txt}                   Denmark {c |}{res}     43,895        0.51       94.52
{txt}                Yugoslavia {c |}{res}     42,500        0.49       95.02
{txt}                      Iran {c |}{res}     39,718        0.46       95.48
{txt}                   Germany {c |}{res}     37,628        0.44       95.92
{txt}                    Poland {c |}{res}     35,644        0.41       96.33
{txt}                      Mena {c |}{res}     33,631        0.39       96.72
{txt}                     Chile {c |}{res}     27,592        0.32       97.05
{txt}             Mediterranian {c |}{res}     26,731        0.31       97.36
{txt}                    Turkey {c |}{res}     25,164        0.29       97.65
{txt}Hungary & Czech & Slovakia {c |}{res}     23,445        0.27       97.92
{txt}                South Asia {c |}{res}     18,357        0.21       98.14
{txt}               East Europe {c |}{res}     18,234        0.21       98.35
{txt} Benelux & Swizz & Austria {c |}{res}     16,753        0.20       98.54
{txt}             South America {c |}{res}     16,601        0.19       98.74
{txt}           South East Asia {c |}{res}     16,379        0.19       98.93
{txt}                 East Asia {c |}{res}     14,714        0.17       99.10
{txt}               US & Canada {c |}{res}     14,700        0.17       99.27
{txt}                    Baltic {c |}{res}     14,321        0.17       99.44
{txt}              UK & Ireland {c |}{res}     11,973        0.14       99.58
{txt}               East Africa {c |}{res}     11,791        0.14       99.71
{txt}                      Iraq {c |}{res}      9,458        0.11       99.82
{txt}              Other Africa {c |}{res}      8,065        0.09       99.92
{txt}           Central America {c |}{res}      4,426        0.05       99.97
{txt}                   Oceania {c |}{res}      1,857        0.02       99.99
{txt}                    Bosnia {c |}{res}        751        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         45        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,589,616      100.00

    {txt}Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,800,378       90.81       90.81
{txt}                   Finland {c |}{res}    217,395        2.53       93.34
{txt}                   Denmark {c |}{res}     43,895        0.51       93.85
{txt}          Norway & Iceland {c |}{res}     57,470        0.67       94.52
{txt}                    Bosnia {c |}{res}        751        0.01       94.53
{txt}                Yugoslavia {c |}{res}     42,500        0.49       95.03
{txt}                    Poland {c |}{res}     35,644        0.41       95.44
{txt}              UK & Ireland {c |}{res}     11,973        0.14       95.58
{txt}                   Germany {c |}{res}     37,628        0.44       96.02
{txt}             Mediterranian {c |}{res}     26,731        0.31       96.33
{txt}                    Baltic {c |}{res}     14,321        0.17       96.50
{txt}               East Europe {c |}{res}     18,234        0.21       96.71
{txt}Hungary & Czech & Slovakia {c |}{res}     23,445        0.27       96.98
{txt} Benelux & Swizz & Austria {c |}{res}     16,753        0.20       97.18
{txt}               US & Canada {c |}{res}     14,700        0.17       97.35
{txt}           Central America {c |}{res}      4,426        0.05       97.40
{txt}                     Chile {c |}{res}     27,592        0.32       97.72
{txt}             South America {c |}{res}     16,601        0.19       97.91
{txt}               East Africa {c |}{res}     11,791        0.14       98.05
{txt}                      Mena {c |}{res}     33,631        0.39       98.44
{txt}              Other Africa {c |}{res}      8,065        0.09       98.54
{txt}                      Iran {c |}{res}     39,718        0.46       99.00
{txt}                      Iraq {c |}{res}      9,458        0.11       99.11
{txt}                    Turkey {c |}{res}     25,164        0.29       99.40
{txt}                 East Asia {c |}{res}     14,714        0.17       99.57
{txt}           South East Asia {c |}{res}     16,379        0.19       99.76
{txt}                South Asia {c |}{res}     18,357        0.21       99.98
{txt}                   Oceania {c |}{res}      1,857        0.02      100.00
{txt}      Unknown or Stateless {c |}{res}         45        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,589,616      100.00
{txt}(789,238 real changes made, 789,238 to missing)
(8,372,221 real changes made, 8,372,221 to missing)
(8,545,721 real changes made, 8,545,721 to missing)
(8,532,146 real changes made, 8,532,146 to missing)
(8,588,865 real changes made, 8,588,865 to missing)
(8,547,116 real changes made, 8,547,116 to missing)
(8,553,972 real changes made, 8,553,972 to missing)
(8,577,643 real changes made, 8,577,643 to missing)
(8,551,988 real changes made, 8,551,988 to missing)
(8,562,885 real changes made, 8,562,885 to missing)
(8,575,295 real changes made, 8,575,295 to missing)
(8,571,382 real changes made, 8,571,382 to missing)
(8,566,171 real changes made, 8,566,171 to missing)
(8,572,863 real changes made, 8,572,863 to missing)
(8,574,916 real changes made, 8,574,916 to missing)
(8,585,190 real changes made, 8,585,190 to missing)
(8,562,024 real changes made, 8,562,024 to missing)
(8,573,015 real changes made, 8,573,015 to missing)
(8,577,825 real changes made, 8,577,825 to missing)
(8,555,985 real changes made, 8,555,985 to missing)
(8,581,551 real changes made, 8,581,551 to missing)
(8,549,898 real changes made, 8,549,898 to missing)
(8,580,158 real changes made, 8,580,158 to missing)
(8,564,452 real changes made, 8,564,452 to missing)
(8,574,902 real changes made, 8,574,902 to missing)
(8,573,237 real changes made, 8,573,237 to missing)
(8,571,259 real changes made, 8,571,259 to missing)
(8,587,759 real changes made, 8,587,759 to missing)
(8,589,571 real changes made, 8,589,571 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}      5,926       10.33       10.33
{txt}                   Finland {c |}{res}      3,583        6.25       16.58
{txt}                   Denmark {c |}{res}      2,496        4.35       20.93
{txt}          Norway & Iceland {c |}{res}      7,318       12.76       33.68
{txt}                    Bosnia {c |}{res}         37        0.06       33.75
{txt}                Yugoslavia {c |}{res}      2,141        3.73       37.48
{txt}                    Poland {c |}{res}      2,057        3.59       41.07
{txt}              UK & Ireland {c |}{res}        975        1.70       42.77
{txt}                   Germany {c |}{res}        754        1.31       44.08
{txt}             Mediterranian {c |}{res}        852        1.49       45.56
{txt}                    Baltic {c |}{res}         89        0.16       45.72
{txt}               East Europe {c |}{res}      2,572        4.48       50.20
{txt}Hungary & Czech & Slovakia {c |}{res}        851        1.48       51.69
{txt} Benelux & Swizz & Austria {c |}{res}        770        1.34       53.03
{txt}               US & Canada {c |}{res}      1,121        1.95       54.98
{txt}           Central America {c |}{res}        627        1.09       56.08
{txt}                     Chile {c |}{res}      1,627        2.84       58.91
{txt}             South America {c |}{res}      1,270        2.21       61.13
{txt}               East Africa {c |}{res}      2,696        4.70       65.83
{txt}                      Mena {c |}{res}      5,933       10.34       76.17
{txt}              Other Africa {c |}{res}        890        1.55       77.72
{txt}                      Iran {c |}{res}      4,414        7.69       85.41
{txt}                      Iraq {c |}{res}      1,870        3.26       88.67
{txt}                    Turkey {c |}{res}      1,679        2.93       91.60
{txt}                 East Asia {c |}{res}      1,058        1.84       93.44
{txt}           South East Asia {c |}{res}      2,134        3.72       97.16
{txt}                South Asia {c |}{res}      1,293        2.25       99.42
{txt}                   Oceania {c |}{res}        333        0.58      100.00
{txt}      Unknown or Stateless {c |}{res}          1        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     57,367      100.00
{txt}(51,441 real changes made, 51,441 to missing)
(53,784 real changes made, 53,784 to missing)
(54,871 real changes made, 54,871 to missing)
(50,049 real changes made, 50,049 to missing)
(57,330 real changes made, 57,330 to missing)
(55,226 real changes made, 55,226 to missing)
(55,310 real changes made, 55,310 to missing)
(56,392 real changes made, 56,392 to missing)
(56,613 real changes made, 56,613 to missing)
(56,515 real changes made, 56,515 to missing)
(57,278 real changes made, 57,278 to missing)
(54,795 real changes made, 54,795 to missing)
(56,516 real changes made, 56,516 to missing)
(56,597 real changes made, 56,597 to missing)
(56,246 real changes made, 56,246 to missing)
(56,740 real changes made, 56,740 to missing)
(55,740 real changes made, 55,740 to missing)
(56,097 real changes made, 56,097 to missing)
(54,671 real changes made, 54,671 to missing)
(51,434 real changes made, 51,434 to missing)
(56,477 real changes made, 56,477 to missing)
(52,953 real changes made, 52,953 to missing)
(55,497 real changes made, 55,497 to missing)
(55,688 real changes made, 55,688 to missing)
(56,309 real changes made, 56,309 to missing)
(55,233 real changes made, 55,233 to missing)
(56,074 real changes made, 56,074 to missing)
(57,034 real changes made, 57,034 to missing)
(57,366 real changes made, 57,366 to missing)
(7,800,378 missing values generated)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,170,720
{txt}{col 9}from master{col 30}{res}       3,391,439{txt}  (_merge==1)
{col 9}from using{col 30}{res}         779,281{txt}  (_merge==2)

{col 5}matched{col 30}{res}       5,199,099{txt}  (_merge==3)
{col 5}{hline 41}

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(2 observations deleted)
(4,113,716 missing values generated)
(3,499,682 missing values generated)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,354,249       93.49       93.49
{txt}                   Finland {c |}{res}    120,669        2.59       96.08
{txt}                   Denmark {c |}{res}     21,786        0.47       96.55
{txt}          Norway & Iceland {c |}{res}     22,943        0.49       97.04
{txt}                    Bosnia {c |}{res}        285        0.01       97.04
{txt}                Yugoslavia {c |}{res}     17,974        0.39       97.43
{txt}                    Poland {c |}{res}     11,054        0.24       97.67
{txt}              UK & Ireland {c |}{res}      3,219        0.07       97.74
{txt}                   Germany {c |}{res}     24,423        0.52       98.26
{txt}             Mediterranian {c |}{res}     12,901        0.28       98.54
{txt}                    Baltic {c |}{res}     11,674        0.25       98.79
{txt}               East Europe {c |}{res}      5,452        0.12       98.91
{txt}Hungary & Czech & Slovakia {c |}{res}     12,886        0.28       99.18
{txt} Benelux & Swizz & Austria {c |}{res}      8,589        0.18       99.37
{txt}               US & Canada {c |}{res}      6,189        0.13       99.50
{txt}           Central America {c |}{res}        407        0.01       99.51
{txt}                     Chile {c |}{res}      2,973        0.06       99.57
{txt}             South America {c |}{res}      2,717        0.06       99.63
{txt}               East Africa {c |}{res}        574        0.01       99.64
{txt}                      Mena {c |}{res}      3,945        0.08       99.73
{txt}              Other Africa {c |}{res}      1,558        0.03       99.76
{txt}                      Iran {c |}{res}        805        0.02       99.78
{txt}                      Iraq {c |}{res}        251        0.01       99.78
{txt}                    Turkey {c |}{res}      5,654        0.12       99.90
{txt}                 East Asia {c |}{res}      1,250        0.03       99.93
{txt}           South East Asia {c |}{res}      1,095        0.02       99.96
{txt}                South Asia {c |}{res}      1,840        0.04       99.99
{txt}                   Oceania {c |}{res}        240        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         14        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  4,657,616      100.00
{txt}(303,367 real changes made, 303,367 to missing)
(4,536,947 real changes made, 4,536,947 to missing)
(4,635,830 real changes made, 4,635,830 to missing)
(4,634,673 real changes made, 4,634,673 to missing)
(4,657,331 real changes made, 4,657,331 to missing)
(4,639,642 real changes made, 4,639,642 to missing)
(4,646,562 real changes made, 4,646,562 to missing)
(4,654,397 real changes made, 4,654,397 to missing)
(4,633,193 real changes made, 4,633,193 to missing)
(4,644,715 real changes made, 4,644,715 to missing)
(4,645,942 real changes made, 4,645,942 to missing)
(4,652,164 real changes made, 4,652,164 to missing)
(4,644,730 real changes made, 4,644,730 to missing)
(4,649,027 real changes made, 4,649,027 to missing)
(4,651,427 real changes made, 4,651,427 to missing)
(4,657,209 real changes made, 4,657,209 to missing)
(4,654,643 real changes made, 4,654,643 to missing)
(4,654,899 real changes made, 4,654,899 to missing)
(4,657,042 real changes made, 4,657,042 to missing)
(4,653,671 real changes made, 4,653,671 to missing)
(4,656,058 real changes made, 4,656,058 to missing)
(4,656,811 real changes made, 4,656,811 to missing)
(4,657,365 real changes made, 4,657,365 to missing)
(4,651,962 real changes made, 4,651,962 to missing)
(4,656,366 real changes made, 4,656,366 to missing)
(4,656,521 real changes made, 4,656,521 to missing)
(4,655,776 real changes made, 4,655,776 to missing)
(4,657,376 real changes made, 4,657,376 to missing)
(4,657,602 real changes made, 4,657,602 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,684,220       91.99       91.99
{txt}                   Finland {c |}{res}    167,283        3.29       95.27
{txt}                   Denmark {c |}{res}     29,141        0.57       95.85
{txt}          Norway & Iceland {c |}{res}     29,685        0.58       96.43
{txt}                    Bosnia {c |}{res}        480        0.01       96.44
{txt}                Yugoslavia {c |}{res}     29,058        0.57       97.01
{txt}                    Poland {c |}{res}     14,614        0.29       97.30
{txt}              UK & Ireland {c |}{res}      4,848        0.10       97.39
{txt}                   Germany {c |}{res}     28,458        0.56       97.95
{txt}             Mediterranian {c |}{res}     17,721        0.35       98.30
{txt}                    Baltic {c |}{res}     12,857        0.25       98.55
{txt}               East Europe {c |}{res}      6,547        0.13       98.68
{txt}Hungary & Czech & Slovakia {c |}{res}     15,539        0.31       98.98
{txt} Benelux & Swizz & Austria {c |}{res}     10,663        0.21       99.19
{txt}               US & Canada {c |}{res}      7,404        0.15       99.34
{txt}           Central America {c |}{res}        596        0.01       99.35
{txt}                     Chile {c |}{res}      3,591        0.07       99.42
{txt}             South America {c |}{res}      3,524        0.07       99.49
{txt}               East Africa {c |}{res}        934        0.02       99.51
{txt}                      Mena {c |}{res}      5,965        0.12       99.63
{txt}              Other Africa {c |}{res}      2,127        0.04       99.67
{txt}                      Iran {c |}{res}      1,525        0.03       99.70
{txt}                      Iraq {c |}{res}        386        0.01       99.70
{txt}                    Turkey {c |}{res}      8,442        0.17       99.87
{txt}                 East Asia {c |}{res}      2,080        0.04       99.91
{txt}           South East Asia {c |}{res}      1,638        0.03       99.94
{txt}                South Asia {c |}{res}      2,548        0.05       99.99
{txt}                   Oceania {c |}{res}        323        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         26        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  5,092,223      100.00
{txt}(408,003 real changes made, 408,003 to missing)
(4,924,940 real changes made, 4,924,940 to missing)
(5,063,082 real changes made, 5,063,082 to missing)
(5,062,538 real changes made, 5,062,538 to missing)
(5,091,743 real changes made, 5,091,743 to missing)
(5,063,165 real changes made, 5,063,165 to missing)
(5,077,609 real changes made, 5,077,609 to missing)
(5,087,375 real changes made, 5,087,375 to missing)
(5,063,765 real changes made, 5,063,765 to missing)
(5,074,502 real changes made, 5,074,502 to missing)
(5,079,366 real changes made, 5,079,366 to missing)
(5,085,676 real changes made, 5,085,676 to missing)
(5,076,684 real changes made, 5,076,684 to missing)
(5,081,560 real changes made, 5,081,560 to missing)
(5,084,819 real changes made, 5,084,819 to missing)
(5,091,627 real changes made, 5,091,627 to missing)
(5,088,632 real changes made, 5,088,632 to missing)
(5,088,699 real changes made, 5,088,699 to missing)
(5,091,289 real changes made, 5,091,289 to missing)
(5,086,258 real changes made, 5,086,258 to missing)
(5,090,096 real changes made, 5,090,096 to missing)
(5,090,698 real changes made, 5,090,698 to missing)
(5,091,837 real changes made, 5,091,837 to missing)
(5,083,781 real changes made, 5,083,781 to missing)
(5,090,143 real changes made, 5,090,143 to missing)
(5,090,585 real changes made, 5,090,585 to missing)
(5,089,675 real changes made, 5,089,675 to missing)
(5,091,900 real changes made, 5,091,900 to missing)
(5,092,197 real changes made, 5,092,197 to missing)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}         793,850
{txt}{col 9}from master{col 30}{res}         791,051{txt}  (_merge==1)
{col 9}from using{col 30}{res}           2,799{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,578,766{txt}  (_merge==3)
{col 5}{hline 41}
(2,799 observations deleted)
(6,138,603 missing values generated)
(9,246,522 missing values generated)
(9,349,373 missing values generated)
(9,346,790 missing values generated)
(9,369,395 missing values generated)
(9,348,575 missing values generated)
(9,354,227 missing values generated)
(9,363,818 missing values generated)
(9,351,523 missing values generated)
(9,357,267 missing values generated)
(9,365,248 missing values generated)
(9,363,665 missing values generated)
(9,358,711 missing values generated)
(9,361,816 missing values generated)
(9,364,713 missing values generated)
(9,368,496 missing values generated)
(9,358,652 missing values generated)
(9,364,548 missing values generated)
(9,366,332 missing values generated)
(9,359,455 missing values generated)
(9,366,416 missing values generated)
(9,359,965 missing values generated)
(9,367,006 missing values generated)
(9,360,138 missing values generated)
(9,365,693 missing values generated)
(9,363,841 missing values generated)
(9,365,188 missing values generated)
(9,369,164 missing values generated)
(9,369,802 missing values generated)
(1,590,711 missing values generated)
(9,153,090 missing values generated)
(9,326,096 missing values generated)
(9,312,757 missing values generated)
(9,369,068 missing values generated)
(9,327,593 missing values generated)
(9,334,413 missing values generated)
(9,357,962 missing values generated)
(9,332,295 missing values generated)
(9,343,257 missing values generated)
(9,355,527 missing values generated)
(9,351,804 missing values generated)
(9,346,456 missing values generated)
(9,353,133 missing values generated)
(9,355,222 missing values generated)
(9,365,438 missing values generated)
(9,342,359 missing values generated)
(9,353,343 missing values generated)
(9,358,243 missing values generated)
(9,336,782 missing values generated)
(9,361,838 missing values generated)
(9,330,528 missing values generated)
(9,360,491 missing values generated)
(9,344,878 missing values generated)
(9,355,210 missing values generated)
(9,353,644 missing values generated)
(9,351,596 missing values generated)
(9,367,996 missing values generated)
(9,369,772 missing values generated)
(4,568,345 missing values generated)
(2,912,084 real changes made, 2,912,084 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  1,691,248       89.52       89.52
{txt}                   Finland {c |}{res}     44,140        2.34       91.85
{txt}                   Denmark {c |}{res}     10,072        0.53       92.39
{txt}          Norway & Iceland {c |}{res}     12,549        0.66       93.05
{txt}                    Bosnia {c |}{res}        155        0.01       93.06
{txt}                Yugoslavia {c |}{res}      9,412        0.50       93.56
{txt}                    Poland {c |}{res}     13,736        0.73       94.28
{txt}              UK & Ireland {c |}{res}      4,547        0.24       94.52
{txt}                   Germany {c |}{res}     14,417        0.76       95.29
{txt}             Mediterranian {c |}{res}      6,043        0.32       95.61
{txt}                    Baltic {c |}{res}      5,051        0.27       95.87
{txt}               East Europe {c |}{res}      6,167        0.33       96.20
{txt}Hungary & Czech & Slovakia {c |}{res}     10,806        0.57       96.77
{txt} Benelux & Swizz & Austria {c |}{res}      7,282        0.39       97.16
{txt}               US & Canada {c |}{res}      5,808        0.31       97.47
{txt}           Central America {c |}{res}      1,008        0.05       97.52
{txt}                     Chile {c |}{res}      4,995        0.26       97.78
{txt}             South America {c |}{res}      3,853        0.20       97.99
{txt}               East Africa {c |}{res}      2,238        0.12       98.11
{txt}                      Mena {c |}{res}      5,327        0.28       98.39
{txt}              Other Africa {c |}{res}      2,247        0.12       98.51
{txt}                      Iran {c |}{res}     13,470        0.71       99.22
{txt}                      Iraq {c |}{res}      2,827        0.15       99.37
{txt}                    Turkey {c |}{res}      2,685        0.14       99.51
{txt}                 East Asia {c |}{res}      3,868        0.20       99.72
{txt}           South East Asia {c |}{res}      1,813        0.10       99.81
{txt}                South Asia {c |}{res}      2,978        0.16       99.97
{txt}                   Oceania {c |}{res}        566        0.03      100.00
{txt}      Unknown or Stateless {c |}{res}          9        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  1,889,317      100.00
{txt}(198,069 real changes made, 198,069 to missing)
(1,845,177 real changes made, 1,845,177 to missing)
(1,879,245 real changes made, 1,879,245 to missing)
(1,876,768 real changes made, 1,876,768 to missing)
(1,889,162 real changes made, 1,889,162 to missing)
(1,879,905 real changes made, 1,879,905 to missing)
(1,875,581 real changes made, 1,875,581 to missing)
(1,884,770 real changes made, 1,884,770 to missing)
(1,874,900 real changes made, 1,874,900 to missing)
(1,883,274 real changes made, 1,883,274 to missing)
(1,884,266 real changes made, 1,884,266 to missing)
(1,883,150 real changes made, 1,883,150 to missing)
(1,878,511 real changes made, 1,878,511 to missing)
(1,882,035 real changes made, 1,882,035 to missing)
(1,883,509 real changes made, 1,883,509 to missing)
(1,888,309 real changes made, 1,888,309 to missing)
(1,884,322 real changes made, 1,884,322 to missing)
(1,885,464 real changes made, 1,885,464 to missing)
(1,887,079 real changes made, 1,887,079 to missing)
(1,883,990 real changes made, 1,883,990 to missing)
(1,887,070 real changes made, 1,887,070 to missing)
(1,875,847 real changes made, 1,875,847 to missing)
(1,886,490 real changes made, 1,886,490 to missing)
(1,886,632 real changes made, 1,886,632 to missing)
(1,885,449 real changes made, 1,885,449 to missing)
(1,887,504 real changes made, 1,887,504 to missing)
(1,886,339 real changes made, 1,886,339 to missing)
(1,888,751 real changes made, 1,888,751 to missing)
(1,889,308 real changes made, 1,889,308 to missing)
file E:\ProjData\Ethnic Enclaves, political participation\temp\enclave_parish90.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,081,864 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(37 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,597,093
{txt}{col 9}from master{col 30}{res}           1,447{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,595,646{txt}  (_merge==2)

{col 5}matched{col 30}{res}          48,398{txt}  (_merge==3)
{col 5}{hline 41}
(1,447 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(207 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,429,112
{txt}{col 9}from master{col 30}{res}             487{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,428,625{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,643,350{txt}  (_merge==3)
{col 5}{hline 41}
(7,428,625 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,830,061       90.60       90.60
{txt}                   Finland {c |}{res}    214,231        2.48       93.07
{txt}          Norway & Iceland {c |}{res}     55,333        0.64       93.71
{txt}                Yugoslavia {c |}{res}     44,083        0.51       94.22
{txt}                      Iran {c |}{res}     43,203        0.50       94.72
{txt}                   Denmark {c |}{res}     42,933        0.50       95.22
{txt}                      Mena {c |}{res}     38,935        0.45       95.67
{txt}                   Germany {c |}{res}     37,341        0.43       96.10
{txt}                    Poland {c |}{res}     36,891        0.43       96.53
{txt}                     Chile {c |}{res}     27,924        0.32       96.85
{txt}             Mediterranian {c |}{res}     26,861        0.31       97.16
{txt}                    Turkey {c |}{res}     26,508        0.31       97.47
{txt}Hungary & Czech & Slovakia {c |}{res}     23,630        0.27       97.74
{txt}               East Europe {c |}{res}     20,886        0.24       97.99
{txt}                South Asia {c |}{res}     19,613        0.23       98.21
{txt}           South East Asia {c |}{res}     18,527        0.21       98.43
{txt}             South America {c |}{res}     17,837        0.21       98.63
{txt} Benelux & Swizz & Austria {c |}{res}     16,917        0.20       98.83
{txt}                 East Asia {c |}{res}     15,551        0.18       99.01
{txt}               East Africa {c |}{res}     15,455        0.18       99.19
{txt}               US & Canada {c |}{res}     15,225        0.18       99.36
{txt}                    Baltic {c |}{res}     14,097        0.16       99.53
{txt}              UK & Ireland {c |}{res}     12,497        0.14       99.67
{txt}                      Iraq {c |}{res}     11,549        0.13       99.81
{txt}              Other Africa {c |}{res}      9,037        0.10       99.91
{txt}           Central America {c |}{res}      4,928        0.06       99.97
{txt}                   Oceania {c |}{res}      2,023        0.02       99.99
{txt}                    Bosnia {c |}{res}        775        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         42        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,642,893      100.00

    {txt}Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  7,830,061       90.60       90.60
{txt}                   Finland {c |}{res}    214,231        2.48       93.07
{txt}                   Denmark {c |}{res}     42,933        0.50       93.57
{txt}          Norway & Iceland {c |}{res}     55,333        0.64       94.21
{txt}                    Bosnia {c |}{res}        775        0.01       94.22
{txt}                Yugoslavia {c |}{res}     44,083        0.51       94.73
{txt}                    Poland {c |}{res}     36,891        0.43       95.16
{txt}              UK & Ireland {c |}{res}     12,497        0.14       95.30
{txt}                   Germany {c |}{res}     37,341        0.43       95.73
{txt}             Mediterranian {c |}{res}     26,861        0.31       96.04
{txt}                    Baltic {c |}{res}     14,097        0.16       96.21
{txt}               East Europe {c |}{res}     20,886        0.24       96.45
{txt}Hungary & Czech & Slovakia {c |}{res}     23,630        0.27       96.72
{txt} Benelux & Swizz & Austria {c |}{res}     16,917        0.20       96.92
{txt}               US & Canada {c |}{res}     15,225        0.18       97.09
{txt}           Central America {c |}{res}      4,928        0.06       97.15
{txt}                     Chile {c |}{res}     27,924        0.32       97.47
{txt}             South America {c |}{res}     17,837        0.21       97.68
{txt}               East Africa {c |}{res}     15,455        0.18       97.86
{txt}                      Mena {c |}{res}     38,935        0.45       98.31
{txt}              Other Africa {c |}{res}      9,037        0.10       98.41
{txt}                      Iran {c |}{res}     43,203        0.50       98.91
{txt}                      Iraq {c |}{res}     11,549        0.13       99.05
{txt}                    Turkey {c |}{res}     26,508        0.31       99.35
{txt}                 East Asia {c |}{res}     15,551        0.18       99.53
{txt}           South East Asia {c |}{res}     18,527        0.21       99.75
{txt}                South Asia {c |}{res}     19,613        0.23       99.98
{txt}                   Oceania {c |}{res}      2,023        0.02      100.00
{txt}      Unknown or Stateless {c |}{res}         42        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  8,642,893      100.00
{txt}(812,832 real changes made, 812,832 to missing)
(8,428,662 real changes made, 8,428,662 to missing)
(8,599,960 real changes made, 8,599,960 to missing)
(8,587,560 real changes made, 8,587,560 to missing)
(8,642,118 real changes made, 8,642,118 to missing)
(8,598,810 real changes made, 8,598,810 to missing)
(8,606,002 real changes made, 8,606,002 to missing)
(8,630,396 real changes made, 8,630,396 to missing)
(8,605,552 real changes made, 8,605,552 to missing)
(8,616,032 real changes made, 8,616,032 to missing)
(8,628,796 real changes made, 8,628,796 to missing)
(8,622,007 real changes made, 8,622,007 to missing)
(8,619,263 real changes made, 8,619,263 to missing)
(8,625,976 real changes made, 8,625,976 to missing)
(8,627,668 real changes made, 8,627,668 to missing)
(8,637,965 real changes made, 8,637,965 to missing)
(8,614,969 real changes made, 8,614,969 to missing)
(8,625,056 real changes made, 8,625,056 to missing)
(8,627,438 real changes made, 8,627,438 to missing)
(8,603,958 real changes made, 8,603,958 to missing)
(8,633,856 real changes made, 8,633,856 to missing)
(8,599,690 real changes made, 8,599,690 to missing)
(8,631,344 real changes made, 8,631,344 to missing)
(8,616,385 real changes made, 8,616,385 to missing)
(8,627,342 real changes made, 8,627,342 to missing)
(8,624,366 real changes made, 8,624,366 to missing)
(8,623,280 real changes made, 8,623,280 to missing)
(8,640,870 real changes made, 8,640,870 to missing)
(8,642,851 real changes made, 8,642,851 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}      4,929       10.21       10.21
{txt}                   Finland {c |}{res}      2,643        5.47       15.68
{txt}                   Denmark {c |}{res}      1,372        2.84       18.52
{txt}          Norway & Iceland {c |}{res}      2,749        5.69       24.22
{txt}                    Bosnia {c |}{res}         28        0.06       24.28
{txt}                Yugoslavia {c |}{res}      1,914        3.96       28.24
{txt}                    Poland {c |}{res}      1,678        3.48       31.72
{txt}              UK & Ireland {c |}{res}        861        1.78       33.50
{txt}                   Germany {c |}{res}        573        1.19       34.69
{txt}             Mediterranian {c |}{res}        724        1.50       36.19
{txt}                    Baltic {c |}{res}        217        0.45       36.64
{txt}               East Europe {c |}{res}      2,947        6.10       42.74
{txt}Hungary & Czech & Slovakia {c |}{res}        543        1.12       43.86
{txt} Benelux & Swizz & Austria {c |}{res}        660        1.37       45.23
{txt}               US & Canada {c |}{res}      1,090        2.26       47.49
{txt}           Central America {c |}{res}        575        1.19       48.68
{txt}                     Chile {c |}{res}        613        1.27       49.95
{txt}             South America {c |}{res}      1,455        3.01       52.96
{txt}               East Africa {c |}{res}      3,726        7.72       60.68
{txt}                      Mena {c |}{res}      5,526       11.45       72.13
{txt}              Other Africa {c |}{res}      1,086        2.25       74.38
{txt}                      Iran {c |}{res}      3,732        7.73       82.10
{txt}                      Iraq {c |}{res}      2,123        4.40       86.50
{txt}                    Turkey {c |}{res}      1,535        3.18       89.68
{txt}                 East Asia {c |}{res}      1,047        2.17       91.85
{txt}           South East Asia {c |}{res}      2,281        4.72       96.57
{txt}                South Asia {c |}{res}      1,376        2.85       99.42
{txt}                   Oceania {c |}{res}        275        0.57       99.99
{txt}      Unknown or Stateless {c |}{res}          3        0.01      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     48,281      100.00
{txt}(43,352 real changes made, 43,352 to missing)
(45,638 real changes made, 45,638 to missing)
(46,909 real changes made, 46,909 to missing)
(45,532 real changes made, 45,532 to missing)
(48,253 real changes made, 48,253 to missing)
(46,367 real changes made, 46,367 to missing)
(46,603 real changes made, 46,603 to missing)
(47,420 real changes made, 47,420 to missing)
(47,708 real changes made, 47,708 to missing)
(47,557 real changes made, 47,557 to missing)
(48,064 real changes made, 48,064 to missing)
(45,334 real changes made, 45,334 to missing)
(47,738 real changes made, 47,738 to missing)
(47,621 real changes made, 47,621 to missing)
(47,191 real changes made, 47,191 to missing)
(47,706 real changes made, 47,706 to missing)
(47,668 real changes made, 47,668 to missing)
(46,826 real changes made, 46,826 to missing)
(44,555 real changes made, 44,555 to missing)
(42,755 real changes made, 42,755 to missing)
(47,195 real changes made, 47,195 to missing)
(44,549 real changes made, 44,549 to missing)
(46,158 real changes made, 46,158 to missing)
(46,746 real changes made, 46,746 to missing)
(47,234 real changes made, 47,234 to missing)
(46,000 real changes made, 46,000 to missing)
(46,905 real changes made, 46,905 to missing)
(48,006 real changes made, 48,006 to missing)
(48,278 real changes made, 48,278 to missing)
(7,830,061 missing values generated)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,402,091
{txt}{col 9}from master{col 30}{res}       3,533,775{txt}  (_merge==1)
{col 9}from using{col 30}{res}         868,316{txt}  (_merge==2)

{col 5}matched{col 30}{res}       5,110,064{txt}  (_merge==3)
{col 5}{hline 41}

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(2 observations deleted)
(4,256,052 missing values generated)
(3,642,018 missing values generated)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,280,762       93.48       93.48
{txt}                   Finland {c |}{res}    119,039        2.60       96.08
{txt}                   Denmark {c |}{res}     21,381        0.47       96.54
{txt}          Norway & Iceland {c |}{res}     22,454        0.49       97.03
{txt}                    Bosnia {c |}{res}        284        0.01       97.04
{txt}                Yugoslavia {c |}{res}     17,857        0.39       97.43
{txt}                    Poland {c |}{res}     10,867        0.24       97.67
{txt}              UK & Ireland {c |}{res}      3,179        0.07       97.74
{txt}                   Germany {c |}{res}     24,024        0.52       98.26
{txt}             Mediterranian {c |}{res}     12,698        0.28       98.54
{txt}                    Baltic {c |}{res}     11,328        0.25       98.79
{txt}               East Europe {c |}{res}      5,326        0.12       98.90
{txt}Hungary & Czech & Slovakia {c |}{res}     12,697        0.28       99.18
{txt} Benelux & Swizz & Austria {c |}{res}      8,444        0.18       99.36
{txt}               US & Canada {c |}{res}      6,025        0.13       99.49
{txt}           Central America {c |}{res}        401        0.01       99.50
{txt}                     Chile {c |}{res}      2,947        0.06       99.57
{txt}             South America {c |}{res}      2,689        0.06       99.63
{txt}               East Africa {c |}{res}        570        0.01       99.64
{txt}                      Mena {c |}{res}      3,927        0.09       99.72
{txt}              Other Africa {c |}{res}      1,536        0.03       99.76
{txt}                      Iran {c |}{res}        800        0.02       99.78
{txt}                      Iraq {c |}{res}        251        0.01       99.78
{txt}                    Turkey {c |}{res}      5,605        0.12       99.90
{txt}                 East Asia {c |}{res}      1,243        0.03       99.93
{txt}           South East Asia {c |}{res}      1,089        0.02       99.95
{txt}                South Asia {c |}{res}      1,825        0.04       99.99
{txt}                   Oceania {c |}{res}        239        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         13        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  4,579,500      100.00
{txt}(298,738 real changes made, 298,738 to missing)
(4,460,461 real changes made, 4,460,461 to missing)
(4,558,119 real changes made, 4,558,119 to missing)
(4,557,046 real changes made, 4,557,046 to missing)
(4,579,216 real changes made, 4,579,216 to missing)
(4,561,643 real changes made, 4,561,643 to missing)
(4,568,633 real changes made, 4,568,633 to missing)
(4,576,321 real changes made, 4,576,321 to missing)
(4,555,476 real changes made, 4,555,476 to missing)
(4,566,802 real changes made, 4,566,802 to missing)
(4,568,172 real changes made, 4,568,172 to missing)
(4,574,174 real changes made, 4,574,174 to missing)
(4,566,803 real changes made, 4,566,803 to missing)
(4,571,056 real changes made, 4,571,056 to missing)
(4,573,475 real changes made, 4,573,475 to missing)
(4,579,099 real changes made, 4,579,099 to missing)
(4,576,553 real changes made, 4,576,553 to missing)
(4,576,811 real changes made, 4,576,811 to missing)
(4,578,930 real changes made, 4,578,930 to missing)
(4,575,573 real changes made, 4,575,573 to missing)
(4,577,964 real changes made, 4,577,964 to missing)
(4,578,700 real changes made, 4,578,700 to missing)
(4,579,249 real changes made, 4,579,249 to missing)
(4,573,895 real changes made, 4,573,895 to missing)
(4,578,257 real changes made, 4,578,257 to missing)
(4,578,411 real changes made, 4,578,411 to missing)
(4,577,675 real changes made, 4,577,675 to missing)
(4,579,261 real changes made, 4,579,261 to missing)
(4,579,487 real changes made, 4,579,487 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  4,601,808       91.97       91.97
{txt}                   Finland {c |}{res}    164,812        3.29       95.27
{txt}                   Denmark {c |}{res}     28,542        0.57       95.84
{txt}          Norway & Iceland {c |}{res}     29,050        0.58       96.42
{txt}                    Bosnia {c |}{res}        481        0.01       96.43
{txt}                Yugoslavia {c |}{res}     28,881        0.58       97.00
{txt}                    Poland {c |}{res}     14,376        0.29       97.29
{txt}              UK & Ireland {c |}{res}      4,790        0.10       97.39
{txt}                   Germany {c |}{res}     27,977        0.56       97.95
{txt}             Mediterranian {c |}{res}     17,447        0.35       98.30
{txt}                    Baltic {c |}{res}     12,449        0.25       98.54
{txt}               East Europe {c |}{res}      6,390        0.13       98.67
{txt}Hungary & Czech & Slovakia {c |}{res}     15,317        0.31       98.98
{txt} Benelux & Swizz & Austria {c |}{res}     10,482        0.21       99.19
{txt}               US & Canada {c |}{res}      7,205        0.14       99.33
{txt}           Central America {c |}{res}        588        0.01       99.34
{txt}                     Chile {c |}{res}      3,559        0.07       99.41
{txt}             South America {c |}{res}      3,487        0.07       99.48
{txt}               East Africa {c |}{res}        931        0.02       99.50
{txt}                      Mena {c |}{res}      5,936        0.12       99.62
{txt}              Other Africa {c |}{res}      2,102        0.04       99.66
{txt}                      Iran {c |}{res}      1,518        0.03       99.69
{txt}                      Iraq {c |}{res}        384        0.01       99.70
{txt}                    Turkey {c |}{res}      8,368        0.17       99.87
{txt}                 East Asia {c |}{res}      2,069        0.04       99.91
{txt}           South East Asia {c |}{res}      1,628        0.03       99.94
{txt}                South Asia {c |}{res}      2,538        0.05       99.99
{txt}                   Oceania {c |}{res}        322        0.01      100.00
{txt}      Unknown or Stateless {c |}{res}         23        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  5,003,460      100.00
{txt}(401,652 real changes made, 401,652 to missing)
(4,838,648 real changes made, 4,838,648 to missing)
(4,974,918 real changes made, 4,974,918 to missing)
(4,974,410 real changes made, 4,974,410 to missing)
(5,002,979 real changes made, 5,002,979 to missing)
(4,974,579 real changes made, 4,974,579 to missing)
(4,989,084 real changes made, 4,989,084 to missing)
(4,998,670 real changes made, 4,998,670 to missing)
(4,975,483 real changes made, 4,975,483 to missing)
(4,986,013 real changes made, 4,986,013 to missing)
(4,991,011 real changes made, 4,991,011 to missing)
(4,997,070 real changes made, 4,997,070 to missing)
(4,988,143 real changes made, 4,988,143 to missing)
(4,992,978 real changes made, 4,992,978 to missing)
(4,996,255 real changes made, 4,996,255 to missing)
(5,002,872 real changes made, 5,002,872 to missing)
(4,999,901 real changes made, 4,999,901 to missing)
(4,999,973 real changes made, 4,999,973 to missing)
(5,002,529 real changes made, 5,002,529 to missing)
(4,997,524 real changes made, 4,997,524 to missing)
(5,001,358 real changes made, 5,001,358 to missing)
(5,001,942 real changes made, 5,001,942 to missing)
(5,003,076 real changes made, 5,003,076 to missing)
(4,995,092 real changes made, 4,995,092 to missing)
(5,001,391 real changes made, 5,001,391 to missing)
(5,001,832 real changes made, 5,001,832 to missing)
(5,000,922 real changes made, 5,000,922 to missing)
(5,003,138 real changes made, 5,003,138 to missing)
(5,003,437 real changes made, 5,003,437 to missing)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}         986,472
{txt}{col 9}from master{col 30}{res}         958,530{txt}  (_merge==1)
{col 9}from using{col 30}{res}          27,942{txt}  (_merge==2)

{col 5}matched{col 30}{res}       8,553,623{txt}  (_merge==3)
{col 5}{hline 41}
(27,942 observations deleted)
(6,289,747 missing values generated)
(9,390,582 missing values generated)
(9,492,401 missing values generated)
(9,490,341 missing values generated)
(9,511,731 missing values generated)
(9,490,960 missing values generated)
(9,496,614 missing values generated)
(9,506,238 missing values generated)
(9,494,008 missing values generated)
(9,499,692 missing values generated)
(9,507,618 missing values generated)
(9,506,023 missing values generated)
(9,501,116 missing values generated)
(9,504,277 missing values generated)
(9,507,133 missing values generated)
(9,510,840 missing values generated)
(9,501,059 missing values generated)
(9,506,915 missing values generated)
(9,508,674 missing values generated)
(9,501,813 missing values generated)
(9,508,767 missing values generated)
(9,502,325 missing values generated)
(9,509,346 missing values generated)
(9,502,500 missing values generated)
(9,508,057 missing values generated)
(9,506,195 missing values generated)
(9,507,552 missing values generated)
(9,511,524 missing values generated)
(9,512,140 missing values generated)
(1,830,353 missing values generated)
(9,300,999 missing values generated)
(9,470,682 missing values generated)
(9,459,781 missing values generated)
(9,511,406 missing values generated)
(9,470,243 missing values generated)
(9,477,167 missing values generated)
(9,500,625 missing values generated)
(9,475,481 missing values generated)
(9,486,154 missing values generated)
(9,498,303 missing values generated)
(9,494,434 missing values generated)
(9,489,143 missing values generated)
(9,495,957 missing values generated)
(9,498,109 missing values generated)
(9,507,847 missing values generated)
(9,484,966 missing values generated)
(9,495,887 missing values generated)
(9,500,636 missing values generated)
(9,479,337 missing values generated)
(9,504,282 missing values generated)
(9,473,099 missing values generated)
(9,502,863 missing values generated)
(9,487,401 missing values generated)
(9,497,749 missing values generated)
(9,496,106 missing values generated)
(9,494,048 missing values generated)
(9,510,436 missing values generated)
(9,512,114 missing values generated)
(4,560,128 missing values generated)
(3,066,678 real changes made, 3,066,678 to missing)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                    Sweden {c |}{res}  1,682,070       89.65       89.65
{txt}                   Finland {c |}{res}     43,117        2.30       91.95
{txt}                   Denmark {c |}{res}      9,589        0.51       92.46
{txt}          Norway & Iceland {c |}{res}     11,741        0.63       93.08
{txt}                    Bosnia {c |}{res}        154        0.01       93.09
{txt}                Yugoslavia {c |}{res}      9,359        0.50       93.59
{txt}                    Poland {c |}{res}     13,623        0.73       94.32
{txt}              UK & Ireland {c |}{res}      4,436        0.24       94.55
{txt}                   Germany {c |}{res}     14,178        0.76       95.31
{txt}             Mediterranian {c |}{res}      5,963        0.32       95.63
{txt}                    Baltic {c |}{res}      4,974        0.27       95.89
{txt}               East Europe {c |}{res}      6,119        0.33       96.22
{txt}Hungary & Czech & Slovakia {c |}{res}     10,690        0.57       96.79
{txt} Benelux & Swizz & Austria {c |}{res}      7,129        0.38       97.17
{txt}               US & Canada {c |}{res}      5,654        0.30       97.47
{txt}           Central America {c |}{res}        995        0.05       97.52
{txt}                     Chile {c |}{res}      4,957        0.26       97.79
{txt}             South America {c |}{res}      3,795        0.20       97.99
{txt}               East Africa {c |}{res}      2,230        0.12       98.11
{txt}                      Mena {c |}{res}      5,288        0.28       98.39
{txt}              Other Africa {c |}{res}      2,214        0.12       98.51
{txt}                      Iran {c |}{res}     13,423        0.72       99.22
{txt}                      Iraq {c |}{res}      2,820        0.15       99.37
{txt}                    Turkey {c |}{res}      2,668        0.14       99.52
{txt}                 East Asia {c |}{res}      3,796        0.20       99.72
{txt}           South East Asia {c |}{res}      1,794        0.10       99.81
{txt}                South Asia {c |}{res}      2,952        0.16       99.97
{txt}                   Oceania {c |}{res}        532        0.03      100.00
{txt}      Unknown or Stateless {c |}{res}          8        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}  1,876,268      100.00
{txt}(194,198 real changes made, 194,198 to missing)
(1,833,151 real changes made, 1,833,151 to missing)
(1,866,679 real changes made, 1,866,679 to missing)
(1,864,527 real changes made, 1,864,527 to missing)
(1,876,114 real changes made, 1,876,114 to missing)
(1,866,909 real changes made, 1,866,909 to missing)
(1,862,645 real changes made, 1,862,645 to missing)
(1,871,832 real changes made, 1,871,832 to missing)
(1,862,090 real changes made, 1,862,090 to missing)
(1,870,305 real changes made, 1,870,305 to missing)
(1,871,294 real changes made, 1,871,294 to missing)
(1,870,149 real changes made, 1,870,149 to missing)
(1,865,578 real changes made, 1,865,578 to missing)
(1,869,139 real changes made, 1,869,139 to missing)
(1,870,614 real changes made, 1,870,614 to missing)
(1,875,273 real changes made, 1,875,273 to missing)
(1,871,311 real changes made, 1,871,311 to missing)
(1,872,473 real changes made, 1,872,473 to missing)
(1,874,038 real changes made, 1,874,038 to missing)
(1,870,980 real changes made, 1,870,980 to missing)
(1,874,054 real changes made, 1,874,054 to missing)
(1,862,845 real changes made, 1,862,845 to missing)
(1,873,448 real changes made, 1,873,448 to missing)
(1,873,600 real changes made, 1,873,600 to missing)
(1,872,472 real changes made, 1,872,472 to missing)
(1,874,474 real changes made, 1,874,474 to missing)
(1,873,316 real changes made, 1,873,316 to missing)
(1,875,736 real changes made, 1,875,736 to missing)
(1,876,260 real changes made, 1,876,260 to missing)
file E:\ProjData\Ethnic Enclaves, political participation\temp\enclave_parish91.dta saved

{com}. 
. foreach y in 87 88 89 90 91 {c -(}
{txt}  2{com}. erase "E:\ProjData\Ethnic Enclaves, political participation\temp\ses-measure19`y'temp.dta"
{txt}  3{com}. {c )-}
{txt}
{com}. 
. 
. 
. 
. 
. ///ALSO PREPARE DATA FOR RADICAL RIGHT PARTY IN 1991
> ///ONLY USED FOR A HETEROGENEITY CHECK IN THE APPENDIX LATER ON
> 
. ///DATA-FILE BELOW HAS NUMBER OF VOTES FOR ALL PARTIES PER ELECTION DISTRICT IN 1991
> use "D:\Data\ExtData\ValResData\Riksdagsvalen1973_2006\0237_rv91.dta"
{txt}
{com}. 
. *keep only the votes for the radical right party "ny demokrati", and information to 
. *create an average voting percentage on municipality level
. keep v13 v7 v16 v26
{txt}
{com}. 
. *district (id-variable) and Municipality
. rename v13 district
{res}{txt}
{com}. rename v7 muni
{res}{txt}
{com}. *Number of votes
. rename v16 Votes
{res}{txt}
{com}. *Votes for "ny demokrati"
. rename v26 Votes_nd
{res}{txt}
{com}. 
. ///Now collapse; to get votes on municipality level
> collapse (sum) Votes Votes_nd, by(muni)
{txt}
{com}. 
. /// gen percentage vote for ND
> gen votes_nd = Votes_nd/Votes
{txt}
{com}. 
. ///for merging puposes replace "muni" as standard integer with different name
> gen Kommun = muni
{txt}
{com}. ///drop non-essential variables
> drop muni Votes Votes_nd
{txt}
{com}. 
. save "E:\ProjData\Ethnic Enclaves, political participation\temp\nd91", replace
{txt}file E:\ProjData\Ethnic Enclaves, political participation\temp\nd91.dta saved

{com}. 
. 
. 
. 
. 
. ///LAST: ALSO PREPARE CONTROL FOR MOVING FIRST YEAR
> ///we add a control for if a person moves or not in arrival year
> foreach y in 87 88 89 90 91 {c -(}
{txt}  2{com}. 
. *open data file with info on all migrations over a large period of time 
. use "RTB\migrationer"
{txt}  3{com}. 
. *Create immigration variable - keep immigrants - drop emmigrations
. 
. tab PostTyp, gen(m)
{txt}  4{com}.         keep if m1==1
{txt}  5{com}.         drop PostTyp m1 m2
{txt}  6{com}.  
. *Generate year and month of arrival - 
. 
. tostring Datum, replace
{txt}  7{com}. gen im_year = substr(Datum, 1, 4) 
{txt}  8{com}. destring im_year, replace
{txt}  9{com}. 
. gen im_month = substr(Datum, 5, 2) 
{txt} 10{com}. destring im_month, replace
{txt} 11{com}. 
. *keep only year in question 
. 
. keep if im_year==19`y'
{txt} 12{com}. 
. *small share who immigrated twise during a year - only need one obs. 
. 
. duplicates tag LopNr, gen(tag)
{txt} 13{com}. sort tag LopNr
{txt} 14{com}. 
. *Keep the observation from the latest immigration month
. bysort LopNr: egen latestmonth = max(im_month)
{txt} 15{com}.         drop if im_month!=latestmonth & tag>0
{txt} 16{com}. *check for duplicates - OK
.         duplicates report LopNr
{txt} 17{com}.         drop tag latestmonth
{txt} 18{com}. 
. ///NOW MERGE DATA ON MOVING - includes all moves over an extended period of time
> merge 1:m LopNr using "RTB/Inrikesflyttar", keepusing(LopNr FryttDat Kommun KommunG)
{txt} 19{com}. 
. drop if _merge==2
{txt} 20{com}. drop _merge
{txt} 21{com}. 
. *Variable for movingdates
. tostring FryttDat, replace
{txt} 22{com}. gen move_year = substr(FryttDat, 1, 4) 
{txt} 23{com}. destring move_year, replace
{txt} 24{com}. 
. gen move_month = substr(FryttDat, 5, 2) 
{txt} 25{com}. destring move_month, replace
{txt} 26{com}. 
. gen move_day = substr(FryttDat, 7, 2) 
{txt} 27{com}. destring move_day, replace
{txt} 28{com}. 
. sort LopNr move_year move_month move_day 
{txt} 29{com}. 
. *Only interested in moves in year`y'
. 
. keep if im_year==move_year
{txt} 30{com}. 
. *Moved out of municipality if municiplaities are different
. gen move_out_tmp = 1 if Kommun!=KommunG
{txt} 31{com}. gen move=1 if Kommun==KommunG
{txt} 32{com}. 
. bysort LopNr: egen move_out_muni = max(move_out_tmp)
{txt} 33{com}. 
. duplicates drop LopNr, force
{txt} 34{com}. 
. 
. keep LopNr move_out_muni move
{txt} 35{com}. save  "E:\ProjData\Ethnic Enclaves, political participation\temp\movers_`y'", replace
{txt} 36{com}. 
. {c )-}

{txt}Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,088,900 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(48 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}42721             0
{txt}{hline 10}{c BT}{hline 27}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      51,951,918
{txt}{col 9}from master{col 30}{res}           5,192{txt}  (_merge==1)
{col 9}from using{col 30}{res}      51,946,726{txt}  (_merge==2)

{col 5}matched{col 30}{res}         222,581{txt}  (_merge==3)
{col 5}{hline 41}
(51,946,726 observations deleted)
FryttDat was {res:long} now {res:str8}
move_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(5192 missing values generated)
{res}{txt}(5,192 missing values generated)
move_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(5192 missing values generated)
{res}{txt}(5,192 missing values generated)
move_day: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(5192 missing values generated)
{res}{txt}(215,698 observations deleted)
(7,687 missing values generated)
(4,388 missing values generated)
(7043 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(1,422 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\movers_87.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,080,362 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(50 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}51257             0
{txt}{hline 10}{c BT}{hline 27}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      51,910,977
{txt}{col 9}from master{col 30}{res}           6,212{txt}  (_merge==1)
{col 9}from using{col 30}{res}      51,904,765{txt}  (_merge==2)

{col 5}matched{col 30}{res}         264,542{txt}  (_merge==3)
{col 5}{hline 41}
(51,904,765 observations deleted)
FryttDat was {res:long} now {res:str8}
move_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(6212 missing values generated)
{res}{txt}(6,212 missing values generated)
move_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(6212 missing values generated)
{res}{txt}(6,212 missing values generated)
move_day: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(6212 missing values generated)
{res}{txt}(254,423 observations deleted)
(9,464 missing values generated)
(6,867 missing values generated)
(8384 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(2,238 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\movers_88.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,065,671 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(76 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}65922             0
{txt}{hline 10}{c BT}{hline 27}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      51,851,817
{txt}{col 9}from master{col 30}{res}           8,961{txt}  (_merge==1)
{col 9}from using{col 30}{res}      51,842,856{txt}  (_merge==2)

{col 5}matched{col 30}{res}         326,451{txt}  (_merge==3)
{col 5}{hline 41}
(51,842,856 observations deleted)
FryttDat was {res:long} now {res:str8}
move_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(8961 missing values generated)
{res}{txt}(8,961 missing values generated)
move_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(8961 missing values generated)
{res}{txt}(8,961 missing values generated)
move_day: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(8961 missing values generated)
{res}{txt}(311,199 observations deleted)
(13,375 missing values generated)
(10,838 missing values generated)
(11431 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(3,557 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\movers_89.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,071,534 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(62 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}60073             0
{txt}{hline 10}{c BT}{hline 27}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      51,897,993
{txt}{col 9}from master{col 30}{res}           9,218{txt}  (_merge==1)
{col 9}from using{col 30}{res}      51,888,775{txt}  (_merge==2)

{col 5}matched{col 30}{res}         280,532{txt}  (_merge==3)
{col 5}{hline 41}
(51,888,775 observations deleted)
FryttDat was {res:long} now {res:str8}
move_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(9218 missing values generated)
{res}{txt}(9,218 missing values generated)
move_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(9218 missing values generated)
{res}{txt}(9,218 missing values generated)
move_day: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(9218 missing values generated)
{res}{txt}(268,474 observations deleted)
(11,376 missing values generated)
(9,900 missing values generated)
(9772 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(3,063 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\movers_90.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,081,864 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(37 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}49768             0
{txt}{hline 10}{c BT}{hline 27}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      51,930,040
{txt}{col 9}from master{col 30}{res}           6,466{txt}  (_merge==1)
{col 9}from using{col 30}{res}      51,923,574{txt}  (_merge==2)

{col 5}matched{col 30}{res}         245,733{txt}  (_merge==3)
{col 5}{hline 41}
(51,923,574 observations deleted)
FryttDat was {res:long} now {res:str8}
move_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(6466 missing values generated)
{res}{txt}(6,466 missing values generated)
move_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(6466 missing values generated)
{res}{txt}(6,466 missing values generated)
move_day: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(6466 missing values generated)
{res}{txt}(236,616 observations deleted)
(7,576 missing values generated)
(8,007 missing values generated)
(6450 missing values generated)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(2,042 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\movers_91.dta saved

{com}. 
. *log close
. 
. 
. 
{txt}end of do-file

{com}. 
. *****************
. * CREATE SAMPLE *
. *****************
. 
. *this file creates the immigrant sample, adds on the relevant ind. variables 
. *and merges family and enclave data. Creates a panel of individuals from 1987-1991
. do "C:\Userdata\Shared\Dofiles\DoData\Ethnic Enclaves, political participation\sample_ind"
{txt}
{com}. ***************************************************
. * 3. MANAGEMENT IND. SAMPLE (REFUGEES 1988-1991)  *
. ***************************************************
. 
. ///SET DIRECTORY/log-files/set more off
> 
. clear all
{res}{txt}
{com}. cd "D:\SCB_ConPol\Stata"
{res}D:\SCB_ConPol\Stata
{txt}
{com}. set more off
{txt}
{com}. 
. *capture log close
. *log using "C:\Userdata\Shared\Logs\Ethnic Enclaves, political participation\log_indsample", replace
. 
. 
. *********************************************
. * CREATE SAMPLE OF IMMIGRANTS FOR 1988-1991 *
. *********************************************
. 
. foreach y in 87 88 89 90 91 {c -(}
{txt}  2{com}. ///OPEN AND PREPARE MIGRATION FILE - basic sample of those immigrating year `y'
> 
. use "RTB\migrationer"
{txt}  3{com}. 
. *Create immigration variable - keep immigrants
. 
. tab PostTyp, gen(m)
{txt}  4{com}.         keep if m1==1
{txt}  5{com}.         drop PostTyp m1 m2
{txt}  6{com}.  
. *Generate year, month of arrival - keep only year in question 
. 
. tostring Datum, replace
{txt}  7{com}. gen im_year = substr(Datum, 1, 4) 
{txt}  8{com}. destring im_year, replace
{txt}  9{com}. 
. gen im_month = substr(Datum, 5, 2) 
{txt} 10{com}. destring im_month, replace
{txt} 11{com}. 
.         keep if im_year==19`y'
{txt} 12{com}. 
. *small share who immigrated twise during a year - drop
. 
. duplicates tag LopNr, gen(tag)
{txt} 13{com}. sort tag LopNr
{txt} 14{com}. 
. *Keep the observation from the latest immigration month
. bysort LopNr: egen latestmonth = max(im_month)
{txt} 15{com}.         drop if im_month!=latestmonth & tag>0
{txt} 16{com}.                 *duplicates report LopNr - OK
.         drop tag latestmonth
{txt} 17{com}. 
. ///MERGE WITH INDIVIDUAL DATA (RTB) - THEY HAVE TO HAVE A PLACE OF RESIDENCE IN SWEDEN
> 
. *Use 1:m beacause there are some id-number duplicates in RTB-file
. merge 1:m LopNr using "RTB\RTB_19`y'"
{txt} 18{com}.         keep if _merge==3
{txt} 19{com}.         drop _merge
{txt} 20{com}. 
. *Small number of duplicates remaining - (19 ind. in 1990, 8 ind 1987 for example) - throw out
. duplicates tag LopNr, gen(tag)
{txt} 21{com}.         drop if tag==1
{txt} 22{com}.         drop tag SenPnr /*no variation*/
{txt} 23{com}. 
.         
. ///MERGE WITH BIRTH COUNTRY DATA - ONLY WANT IMM FROM CERTAIN COUNTRIES
> merge 1:m LopNr using "RTB\FodelselandIFAUGrupp"
{txt} 24{com}.         keep if _merge==3
{txt} 25{com}.         drop _merge SenPnr /*no variation*/
{txt} 26{com}. 
. ***Now create fill in the information on country of birth (labels)
. 
. lab def IFAUkod 0 "Sweden"
{txt} 27{com}. lab def IFAUkod 26 "Finland", add
{txt} 28{com}. lab def IFAUkod 27 "Denmark", add
{txt} 29{com}. lab def IFAUkod 28 "Norway & Iceland", add
{txt} 30{com}. lab def IFAUkod 29 "Bosnia", add
{txt} 31{com}. lab def IFAUkod 30 "Yugoslavia", add
{txt} 32{com}. lab def IFAUkod 31 "Poland", add
{txt} 33{com}. lab def IFAUkod 32 "UK & Ireland", add
{txt} 34{com}. lab def IFAUkod 33 "Germany", add
{txt} 35{com}. lab def IFAUkod 34 "Mediterranian", add
{txt} 36{com}. lab def IFAUkod 35 "Baltic", add
{txt} 37{com}. lab def IFAUkod 36 "East Europe", add
{txt} 38{com}. lab def IFAUkod 37 "Hungary & Czech & Slovakia", add
{txt} 39{com}. lab def IFAUkod 38 "Benelux & Swizz & Austria", add
{txt} 40{com}. lab def IFAUkod 39 "US & Canada", add
{txt} 41{com}. lab def IFAUkod 40 "Central America", add
{txt} 42{com}. lab def IFAUkod 41 "Chile", add
{txt} 43{com}. lab def IFAUkod 42 "South America", add
{txt} 44{com}. lab def IFAUkod 43 "East Africa", add
{txt} 45{com}. lab def IFAUkod 44 "Mena", add
{txt} 46{com}. lab def IFAUkod 45 "Other Africa", add
{txt} 47{com}. lab def IFAUkod 46 "Iran", add
{txt} 48{com}. lab def IFAUkod 47 "Iraq", add  
{txt} 49{com}. lab def IFAUkod 48 "Turkey", add
{txt} 50{com}. lab def IFAUkod 49 "East Asia", add
{txt} 51{com}. lab def IFAUkod 50 "South East Asia", add
{txt} 52{com}. lab def IFAUkod 51 "South Asia", add
{txt} 53{com}. lab def IFAUkod 52 "Oceania", add
{txt} 54{com}. lab def IFAUkod 53 "Unknown or Stateless", add
{txt} 55{com}. 
. lab values IFAUkod IFAUkod      
{txt} 56{com}. 
. tab IFAUkod, sort
{txt} 57{com}. 
.         ///NOW DROP THOSE COMING FROM COUNTRIES WHERE REFUGEES WERE VERY RARE
> 
. *Missing birth country value: 
.         drop if IFAUkod==.
{txt} 58{com}. *Nordics: 
.         drop if IFAUkod ==0 | IFAUkod==26 | IFAUkod==27 | IFAUkod==28 
{txt} 59{com}. *Europeans:     
.         drop if IFAUkod>30 & IFAUkod<39
{txt} 60{com}. *Americans: 
.         drop if IFAUkod==39
{txt} 61{com}. *Oceania: 
.         drop if IFAUkod==52
{txt} 62{com}. *Unknown/stateless: 
. capture drop if IFAUkod==53
{txt} 63{com}.         
. ///CAN MERGE ON DEATH DATE (NOT USED CURRENTLY)
> 
. *1:m cause some die twise apparently
. *merge 1:m LopNr using "RTB\DodDat", keepusing(DodDatum SenPnr)
. *       drop if _merge==2
. *       drop _merge SenPnr
.         *duplicates report LopNr - OK
.         
.         ///CAN MERGE ON SAMS-DATA (NOT USED)
> *merge 1:m LopNr using "SAMS\SAMS_19`y'"
. *Keep only those with information
. *keep if _merge==3
.         *duplicates report - OK
. *       drop _merge
. 
. 
. 
. ///MERGE ON MORE BIRTH DATA TO GET SEX AND AGE
> *1:m again
. merge 1:m LopNr using "RTB\Fodelseuppg", keepusing(Kon UtlSvBakG FodArMan)
{txt} 64{com}.         keep if _merge==3
{txt} 65{com}.         duplicates report LopNr
{txt} 66{com}.         drop _merge 
{txt} 67{com}. 
. *Rename sex-variable and make Men= 1, women= 0.
. rename Kon sex
{txt} 68{com}. replace sex=0 if sex==2
{txt} 69{com}. 
.         
. ///CREATE YEAR AND AGE VARIABLE
> gen year=19`y'
{txt} 70{com}. 
. tostring FodArMan, replace
{txt} 71{com}. gen year_born = substr(FodArMan, 1, 4) 
{txt} 72{com}. destring year_born, replace
{txt} 73{com}. 
. gen age =year-year_born
{txt} 74{com}. 
. save  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'", replace
{txt} 75{com}. 
. {c )-}

{txt}Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,088,900 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(48 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,373,758
{txt}{col 9}from master{col 30}{res}           1,197{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,372,561{txt}  (_merge==2)

{col 5}matched{col 30}{res}          41,528{txt}  (_merge==3)
{col 5}{hline 41}
(8,373,758 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(8 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,030,461
{txt}{col 9}from master{col 30}{res}               3{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,030,458{txt}  (_merge==2)

{col 5}matched{col 30}{res}          41,517{txt}  (_merge==3)
{col 5}{hline 41}
(16,030,461 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                      Iran {c |}{res}      7,396       17.83       17.83
{txt}                    Sweden {c |}{res}      4,926       11.87       29.70
{txt}                   Finland {c |}{res}      4,200       10.12       39.83
{txt}                     Chile {c |}{res}      2,767        6.67       46.50
{txt}          Norway & Iceland {c |}{res}      2,546        6.14       52.63
{txt}                      Mena {c |}{res}      2,103        5.07       57.70
{txt}                    Poland {c |}{res}      1,597        3.85       61.55
{txt}                    Turkey {c |}{res}      1,313        3.17       64.72
{txt}               East Africa {c |}{res}      1,290        3.11       67.83
{txt}               East Europe {c |}{res}      1,156        2.79       70.62
{txt}                South Asia {c |}{res}      1,149        2.77       73.38
{txt}                   Denmark {c |}{res}      1,099        2.65       76.03
{txt}             South America {c |}{res}      1,073        2.59       78.62
{txt}               US & Canada {c |}{res}        951        2.29       80.91
{txt}                Yugoslavia {c |}{res}        942        2.27       83.18
{txt}           South East Asia {c |}{res}        915        2.21       85.39
{txt}                 East Asia {c |}{res}        903        2.18       87.57
{txt}             Mediterranian {c |}{res}        863        2.08       89.65
{txt}              UK & Ireland {c |}{res}        760        1.83       91.48
{txt}              Other Africa {c |}{res}        668        1.61       93.09
{txt}                   Germany {c |}{res}        625        1.51       94.60
{txt} Benelux & Swizz & Austria {c |}{res}        614        1.48       96.08
{txt}Hungary & Czech & Slovakia {c |}{res}        559        1.35       97.42
{txt}                      Iraq {c |}{res}        442        1.07       98.49
{txt}           Central America {c |}{res}        353        0.85       99.34
{txt}                   Oceania {c |}{res}        210        0.51       99.85
{txt}                    Baltic {c |}{res}         40        0.10       99.94
{txt}                    Bosnia {c |}{res}         24        0.06      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     41,484      100.00
{txt}(33 observations deleted)
(12,771 observations deleted)
(6,214 observations deleted)
(951 observations deleted)
(210 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,051,282
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,051,282{txt}  (_merge==2)

{col 5}matched{col 30}{res}          21,338{txt}  (_merge==3)
{col 5}{hline 41}
(16,051,282 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}21338             0
{txt}{hline 10}{c BT}{hline 27}
{res}{txt}(10,015 real changes made)
FodArMan was {res:long} now {res:str6}
year_born: all characters numeric; {res}replaced {txt}as {res}int
{txt}file E:\ProjData\Ethnic Enclaves, political participation\temp\temp87.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,080,362 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(50 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,410,622
{txt}{col 9}from master{col 30}{res}           1,502{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,409,120{txt}  (_merge==2)

{col 5}matched{col 30}{res}          49,766{txt}  (_merge==3)
{col 5}{hline 41}
(8,410,622 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(22 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,022,233
{txt}{col 9}from master{col 30}{res}               1{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,022,232{txt}  (_merge==2)

{col 5}matched{col 30}{res}          49,743{txt}  (_merge==3)
{col 5}{hline 41}
(16,022,233 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                      Iran {c |}{res}      8,006       16.11       16.11
{txt}                    Sweden {c |}{res}      6,072       12.22       28.33
{txt}                   Finland {c |}{res}      4,676        9.41       37.73
{txt}          Norway & Iceland {c |}{res}      3,432        6.91       44.64
{txt}                      Mena {c |}{res}      3,050        6.14       50.78
{txt}                     Chile {c |}{res}      2,916        5.87       56.64
{txt}                   Denmark {c |}{res}      2,067        4.16       60.80
{txt}               East Europe {c |}{res}      1,760        3.54       64.34
{txt}               East Africa {c |}{res}      1,625        3.27       67.61
{txt}                    Poland {c |}{res}      1,621        3.26       70.88
{txt}           South East Asia {c |}{res}      1,449        2.92       73.79
{txt}                      Iraq {c |}{res}      1,249        2.51       76.30
{txt}                    Turkey {c |}{res}      1,235        2.48       78.79
{txt}                Yugoslavia {c |}{res}      1,110        2.23       81.02
{txt}                South Asia {c |}{res}      1,049        2.11       83.13
{txt}             South America {c |}{res}        989        1.99       85.12
{txt}                 East Asia {c |}{res}        987        1.99       87.11
{txt}               US & Canada {c |}{res}        938        1.89       89.00
{txt}              UK & Ireland {c |}{res}        932        1.88       90.87
{txt}             Mediterranian {c |}{res}        868        1.75       92.62
{txt} Benelux & Swizz & Austria {c |}{res}        756        1.52       94.14
{txt}Hungary & Czech & Slovakia {c |}{res}        713        1.43       95.57
{txt}              Other Africa {c |}{res}        685        1.38       96.95
{txt}                   Germany {c |}{res}        637        1.28       98.23
{txt}           Central America {c |}{res}        501        1.01       99.24
{txt}                   Oceania {c |}{res}        293        0.59       99.83
{txt}                    Baltic {c |}{res}         61        0.12       99.95
{txt}                    Bosnia {c |}{res}         21        0.04      100.00
{txt}      Unknown or Stateless {c |}{res}          2        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     49,700      100.00
{txt}(43 observations deleted)
(16,247 observations deleted)
(7,348 observations deleted)
(938 observations deleted)
(293 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,047,748
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,047,748{txt}  (_merge==2)

{col 5}matched{col 30}{res}          24,872{txt}  (_merge==3)
{col 5}{hline 41}
(16,047,748 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}24872             0
{txt}{hline 10}{c BT}{hline 27}
{res}{txt}(11,584 real changes made)
FodArMan was {res:long} now {res:str6}
year_born: all characters numeric; {res}replaced {txt}as {res}int
{txt}file E:\ProjData\Ethnic Enclaves, political participation\temp\temp88.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,065,671 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(76 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,466,017
{txt}{col 9}from master{col 30}{res}           2,459{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,463,558{txt}  (_merge==2)

{col 5}matched{col 30}{res}          63,480{txt}  (_merge==3)
{col 5}{hline 41}
(8,466,017 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(34 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,008,537
{txt}{col 9}from master{col 30}{res}               4{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,008,533{txt}  (_merge==2)

{col 5}matched{col 30}{res}          63,442{txt}  (_merge==3)
{col 5}{hline 41}
(16,008,537 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
          Norway & Iceland {c |}{res}      8,932       14.09       14.09
{txt}                      Iran {c |}{res}      6,816       10.75       24.84
{txt}                    Sweden {c |}{res}      6,259        9.87       34.72
{txt}                      Mena {c |}{res}      6,123        9.66       44.37
{txt}                     Chile {c |}{res}      5,837        9.21       53.58
{txt}                   Finland {c |}{res}      3,941        6.22       59.80
{txt}                   Denmark {c |}{res}      3,638        5.74       65.54
{txt}               East Africa {c |}{res}      2,051        3.24       68.77
{txt}               East Europe {c |}{res}      2,016        3.18       71.95
{txt}                    Poland {c |}{res}      1,755        2.77       74.72
{txt}                    Turkey {c |}{res}      1,645        2.59       77.32
{txt}                Yugoslavia {c |}{res}      1,598        2.52       79.84
{txt}           South East Asia {c |}{res}      1,450        2.29       82.13
{txt}                      Iraq {c |}{res}      1,259        1.99       84.11
{txt}                South Asia {c |}{res}      1,175        1.85       85.97
{txt}             South America {c |}{res}      1,142        1.80       87.77
{txt}Hungary & Czech & Slovakia {c |}{res}      1,086        1.71       89.48
{txt}               US & Canada {c |}{res}        995        1.57       91.05
{txt}                 East Asia {c |}{res}        925        1.46       92.51
{txt}             Mediterranian {c |}{res}        895        1.41       93.92
{txt}              UK & Ireland {c |}{res}        835        1.32       95.24
{txt}              Other Africa {c |}{res}        789        1.24       96.48
{txt}                   Germany {c |}{res}        770        1.21       97.70
{txt} Benelux & Swizz & Austria {c |}{res}        618        0.97       98.67
{txt}           Central America {c |}{res}        470        0.74       99.41
{txt}                   Oceania {c |}{res}        264        0.42       99.83
{txt}                    Baltic {c |}{res}         57        0.09       99.92
{txt}                    Bosnia {c |}{res}         47        0.07       99.99
{txt}      Unknown or Stateless {c |}{res}          4        0.01      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     63,392      100.00
{txt}(50 observations deleted)
(22,770 observations deleted)
(8,032 observations deleted)
(995 observations deleted)
(264 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,041,293
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,041,293{txt}  (_merge==2)

{col 5}matched{col 30}{res}          31,327{txt}  (_merge==3)
{col 5}{hline 41}
(16,041,293 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}31327             0
{txt}{hline 10}{c BT}{hline 27}
{res}{txt}(14,315 real changes made)
FodArMan was {res:long} now {res:str6}
year_born: all characters numeric; {res}replaced {txt}as {res}int
{txt}file E:\ProjData\Ethnic Enclaves, political participation\temp\temp89.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,071,534 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(62 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,535,856
{txt}{col 9}from master{col 30}{res}           2,661{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,533,195{txt}  (_merge==2)

{col 5}matched{col 30}{res}          57,431{txt}  (_merge==3)
{col 5}{hline 41}
(8,535,856 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(38 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,014,592
{txt}{col 9}from master{col 30}{res}               5{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,014,587{txt}  (_merge==2)

{col 5}matched{col 30}{res}          57,388{txt}  (_merge==3)
{col 5}{hline 41}
(16,014,592 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
          Norway & Iceland {c |}{res}      7,317       12.76       12.76
{txt}                      Mena {c |}{res}      5,928       10.34       23.10
{txt}                    Sweden {c |}{res}      5,926       10.33       33.43
{txt}                      Iran {c |}{res}      4,408        7.69       41.12
{txt}                   Finland {c |}{res}      3,583        6.25       47.36
{txt}               East Africa {c |}{res}      2,694        4.70       52.06
{txt}               East Europe {c |}{res}      2,571        4.48       56.54
{txt}                   Denmark {c |}{res}      2,496        4.35       60.90
{txt}                Yugoslavia {c |}{res}      2,140        3.73       64.63
{txt}           South East Asia {c |}{res}      2,133        3.72       68.35
{txt}                    Poland {c |}{res}      2,056        3.59       71.93
{txt}                      Iraq {c |}{res}      1,869        3.26       75.19
{txt}                    Turkey {c |}{res}      1,679        2.93       78.12
{txt}                     Chile {c |}{res}      1,627        2.84       80.96
{txt}                South Asia {c |}{res}      1,293        2.25       83.21
{txt}             South America {c |}{res}      1,270        2.21       85.43
{txt}               US & Canada {c |}{res}      1,121        1.95       87.38
{txt}                 East Asia {c |}{res}      1,058        1.84       89.23
{txt}              UK & Ireland {c |}{res}        975        1.70       90.93
{txt}              Other Africa {c |}{res}        890        1.55       92.48
{txt}             Mediterranian {c |}{res}        852        1.49       93.96
{txt}Hungary & Czech & Slovakia {c |}{res}        851        1.48       95.45
{txt} Benelux & Swizz & Austria {c |}{res}        770        1.34       96.79
{txt}                   Germany {c |}{res}        754        1.31       98.10
{txt}           Central America {c |}{res}        627        1.09       99.20
{txt}                   Oceania {c |}{res}        333        0.58       99.78
{txt}                    Baltic {c |}{res}         89        0.16       99.93
{txt}                    Bosnia {c |}{res}         37        0.06      100.00
{txt}      Unknown or Stateless {c |}{res}          1        0.00      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     57,348      100.00
{txt}(40 observations deleted)
(19,322 observations deleted)
(8,918 observations deleted)
(1,121 observations deleted)
(333 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,044,967
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,044,967{txt}  (_merge==2)

{col 5}matched{col 30}{res}          27,653{txt}  (_merge==3)
{col 5}{hline 41}
(16,044,967 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}27653             0
{txt}{hline 10}{c BT}{hline 27}
{res}{txt}(13,321 real changes made)
FodArMan was {res:long} now {res:str6}
year_born: all characters numeric; {res}replaced {txt}as {res}int
{txt}file E:\ProjData\Ethnic Enclaves, political participation\temp\temp90.dta saved

Record type {c |}
 of input / {c |}
 emigration {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        Inv {c |}{res}  3,131,669       62.62       62.62
{txt}        Utv {c |}{res}  1,869,473       37.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}  5,001,142      100.00
{txt}(1,869,473 observations deleted)
Datum was {res:long} now {res:str8}
im_year: all characters numeric; {res}replaced {txt}as {res}int
{txt}(28 missing values generated)
im_month: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(28 missing values generated)
{res}{txt}(3,081,864 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(37 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       8,597,093
{txt}{col 9}from master{col 30}{res}           1,447{txt}  (_merge==1)
{col 9}from using{col 30}{res}       8,595,646{txt}  (_merge==2)

{col 5}matched{col 30}{res}          48,398{txt}  (_merge==3)
{col 5}{hline 41}
(8,597,093 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(154 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,023,737
{txt}{col 9}from master{col 30}{res}               3{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,023,734{txt}  (_merge==2)

{col 5}matched{col 30}{res}          48,241{txt}  (_merge==3)
{col 5}{hline 41}
(16,023,737 observations deleted)

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                      Mena {c |}{res}      5,519       11.45       11.45
{txt}                    Sweden {c |}{res}      4,909       10.18       21.63
{txt}                      Iran {c |}{res}      3,723        7.72       29.36
{txt}               East Africa {c |}{res}      3,712        7.70       37.06
{txt}               East Europe {c |}{res}      2,941        6.10       43.16
{txt}          Norway & Iceland {c |}{res}      2,748        5.70       48.86
{txt}                   Finland {c |}{res}      2,641        5.48       54.34
{txt}           South East Asia {c |}{res}      2,279        4.73       59.07
{txt}                      Iraq {c |}{res}      2,121        4.40       63.47
{txt}                Yugoslavia {c |}{res}      1,913        3.97       67.43
{txt}                    Poland {c |}{res}      1,678        3.48       70.92
{txt}                    Turkey {c |}{res}      1,534        3.18       74.10
{txt}             South America {c |}{res}      1,455        3.02       77.12
{txt}                South Asia {c |}{res}      1,375        2.85       79.97
{txt}                   Denmark {c |}{res}      1,371        2.84       82.81
{txt}               US & Canada {c |}{res}      1,089        2.26       85.07
{txt}              Other Africa {c |}{res}      1,084        2.25       87.32
{txt}                 East Asia {c |}{res}      1,044        2.17       89.49
{txt}              UK & Ireland {c |}{res}        861        1.79       91.27
{txt}             Mediterranian {c |}{res}        723        1.50       92.77
{txt} Benelux & Swizz & Austria {c |}{res}        660        1.37       94.14
{txt}                     Chile {c |}{res}        613        1.27       95.41
{txt}                   Germany {c |}{res}        573        1.19       96.60
{txt}           Central America {c |}{res}        573        1.19       97.79
{txt}Hungary & Czech & Slovakia {c |}{res}        543        1.13       98.92
{txt}                   Oceania {c |}{res}        274        0.57       99.49
{txt}                    Baltic {c |}{res}        217        0.45       99.94
{txt}                    Bosnia {c |}{res}         28        0.06       99.99
{txt}      Unknown or Stateless {c |}{res}          3        0.01      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     48,204      100.00
{txt}(37 observations deleted)
(11,669 observations deleted)
(8,196 observations deleted)
(1,089 observations deleted)
(274 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}      16,045,647
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}      16,045,647{txt}  (_merge==2)

{col 5}matched{col 30}{res}          26,973{txt}  (_merge==3)
{col 5}{hline 41}
(16,045,647 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}{hline 10}{c TT}{hline 27}
   copies {c |} observations       surplus
{hline 10}{c +}{hline 27}
        1 {c |}        {res}26973             0
{txt}{hline 10}{c BT}{hline 27}
{res}{txt}(12,823 real changes made)
FodArMan was {res:long} now {res:str6}
year_born: all characters numeric; {res}replaced {txt}as {res}int
{txt}file E:\ProjData\Ethnic Enclaves, political participation\temp\temp91.dta saved

{com}. 
. 
. ///MERGE ON LISA DATA FOR SOCIOECONOMIC STUFF - note that this exists from 1990
> *hence for the years up to 1990, use the file from 1990
. 
. foreach y in 87 88 89 90 {c -(}
{txt}  2{com}. 
. use  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'"
{txt}  3{com}. 
. *Duplicates again
. merge 1:m LopNr using "LISA\LISA_1990"
{txt}  4{com}. *Keep only those with information - other are not immigrants in year `y'
. drop if _merge==2
{txt}  5{com}. *Duplicates a few people - drop (for example 12 obs 1987)
.         duplicates tag LopNr, gen(tag)
{txt}  6{com}.         drop if tag==1
{txt}  7{com}.         drop tag _merge
{txt}  8{com}.         
. save  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'", replace
{txt}  9{com}. 
. {c )-}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,926,755
{txt}{col 9}from master{col 30}{res}           6,154{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,920,601{txt}  (_merge==2)

{col 5}matched{col 30}{res}          15,190{txt}  (_merge==3)
{col 5}{hline 41}
(6,920,601 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(12 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp87.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,924,829
{txt}{col 9}from master{col 30}{res}           6,958{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,917,871{txt}  (_merge==2)

{col 5}matched{col 30}{res}          17,920{txt}  (_merge==3)
{col 5}{hline 41}
(6,917,871 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(12 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp88.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,922,186
{txt}{col 9}from master{col 30}{res}           8,862{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,913,324{txt}  (_merge==2)

{col 5}matched{col 30}{res}          22,467{txt}  (_merge==3)
{col 5}{hline 41}
(6,913,324 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(4 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp89.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,924,939
{txt}{col 9}from master{col 30}{res}           8,401{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,916,538{txt}  (_merge==2)

{col 5}matched{col 30}{res}          19,253{txt}  (_merge==3)
{col 5}{hline 41}
(6,916,538 observations deleted)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}(2 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp90.dta saved

{com}. 
. ///1991
> 
. use  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp91"
{txt}
{com}. 
. *Duplicates again
. merge 1:m LopNr using "LISA\LISA_1991"
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,954,480
{txt}{col 9}from master{col 30}{res}           8,037{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,946,443{txt}  (_merge==2)

{col 5}matched{col 30}{res}          18,936{txt}  (_merge==3)
{col 5}{hline 41}

{com}. *Keep only those with information
. drop if _merge==2
{txt}(6,946,443 observations deleted)

{com}. *Duplicates 1 person - drop
.         duplicates tag LopNr, gen(tag)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}
{com}.         drop if tag==1
{txt}(0 observations deleted)

{com}.         drop tag _merge
{txt}
{com}.         
. save  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp91", replace
{txt}file E:\ProjData\Ethnic Enclaves, political participation\temp\temp91.dta saved

{com}. 
. 
. *********************************************************************************
. * NOW MERGE ON FAMILY DATA TO DROP THOSE WITH SIBLINGS OR FAMILY IN THE COUNTRY *
. *********************************************************************************
. 
. foreach y in 87 88 89 90 91 {c -(}
{txt}  2{com}. ///Siblingdata
> 
. use  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'"
{txt}  3{com}. gen sample=1
{txt}  4{com}.         
.         merge 1:1 LopNr using  "E:\ProjData\Ethnic Enclaves, political participation\temp\hushåll`y'"
{txt}  5{com}.         keep if sample==1
{txt}  6{com}.         drop _merge
{txt}  7{com}.         
.                 merge 1:1 LopNr using  "E:\ProjData\Ethnic Enclaves, political participation\temp\syskon`y'"
{txt}  8{com}.         keep if sample==1
{txt}  9{com}.         drop _merge
{txt} 10{com}. 
. 
. *drop if family
.         drop if anhorig==1
{txt} 11{com}.         drop if sibling_im==1
{txt} 12{com}.         drop sample
{txt} 13{com}. 
. save  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'", replace
{txt} 14{com}.         
. {c )-}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,545,246
{txt}{col 9}from master{col 30}{res}          16,800{txt}  (_merge==1)
{col 9}from using{col 30}{res}       4,528,446{txt}  (_merge==2)

{col 5}matched{col 30}{res}           4,532{txt}  (_merge==3)
{col 5}{hline 41}
(4,528,446 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}          21,805
{txt}{col 9}from master{col 30}{res}          20,365{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,440{txt}  (_merge==2)

{col 5}matched{col 30}{res}             967{txt}  (_merge==3)
{col 5}{hline 41}
(1,440 observations deleted)
(3,363 observations deleted)
(830 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp87.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,564,188
{txt}{col 9}from master{col 30}{res}          19,593{txt}  (_merge==1)
{col 9}from using{col 30}{res}       4,544,595{txt}  (_merge==2)

{col 5}matched{col 30}{res}           5,273{txt}  (_merge==3)
{col 5}{hline 41}
(4,544,595 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}          25,630
{txt}{col 9}from master{col 30}{res}          23,978{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,652{txt}  (_merge==2)

{col 5}matched{col 30}{res}             888{txt}  (_merge==3)
{col 5}{hline 41}
(1,652 observations deleted)
(4,131 observations deleted)
(764 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp88.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,588,859
{txt}{col 9}from master{col 30}{res}          25,688{txt}  (_merge==1)
{col 9}from using{col 30}{res}       4,563,171{txt}  (_merge==2)

{col 5}matched{col 30}{res}           5,637{txt}  (_merge==3)
{col 5}{hline 41}
(4,563,171 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}          32,030
{txt}{col 9}from master{col 30}{res}          30,298{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,732{txt}  (_merge==2)

{col 5}matched{col 30}{res}           1,027{txt}  (_merge==3)
{col 5}{hline 41}
(1,732 observations deleted)
(4,554 observations deleted)
(885 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp89.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,608,391
{txt}{col 9}from master{col 30}{res}          22,679{txt}  (_merge==1)
{col 9}from using{col 30}{res}       4,585,712{txt}  (_merge==2)

{col 5}matched{col 30}{res}           4,973{txt}  (_merge==3)
{col 5}{hline 41}
(4,585,712 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}          28,157
{txt}{col 9}from master{col 30}{res}          26,505{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,652{txt}  (_merge==2)

{col 5}matched{col 30}{res}           1,147{txt}  (_merge==3)
{col 5}{hline 41}
(1,652 observations deleted)
(4,973 observations deleted)
(1,003 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp90.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       4,600,304
{txt}{col 9}from master{col 30}{res}          22,068{txt}  (_merge==1)
{col 9}from using{col 30}{res}       4,578,236{txt}  (_merge==2)

{col 5}matched{col 30}{res}           4,905{txt}  (_merge==3)
{col 5}{hline 41}
(4,578,236 observations deleted)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}          27,335
{txt}{col 9}from master{col 30}{res}          25,940{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,395{txt}  (_merge==2)

{col 5}matched{col 30}{res}           1,033{txt}  (_merge==3)
{col 5}{hline 41}
(1,395 observations deleted)
(4,905 observations deleted)
(873 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp91.dta saved

{com}. 
. 
. ***************************
. *ADD ENCLAVE DATA *
. **************************
. foreach y in 87 88 89 90 91 {c -(}
{txt}  2{com}. use  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'"
{txt}  3{com}. merge m:1 Forsamling using  "E:\ProjData\Ethnic Enclaves, political participation\temp\enclave_parish`y'"
{txt}  4{com}. 
. drop if LopNr==.
{txt}  5{com}. drop _merge
{txt}  6{com}. 
. gen coethnics = country2 if IFAUkod==2+24
{txt}  7{com}. foreach m of numlist 2(1)29 {c -(}
{txt}  8{com}. replace coethnics = country`m' if IFAUkod==`m'+24
{txt}  9{com}. {c )-} 
{txt} 10{com}. 
. gen voted = voted2 if IFAUkod==2+24
{txt} 11{com}. foreach e of numlist 2(1)29 {c -(}
{txt} 12{com}. replace voted = voted`e' if IFAUkod==`e'+24
{txt} 13{com}. {c )-} 
{txt} 14{com}. 
. gen right = right2 if IFAUkod==2+24
{txt} 15{com}. foreach g of numlist 2(1)29 {c -(}
{txt} 16{com}. replace right = right`g' if IFAUkod==`g'+24
{txt} 17{com}. {c )-} 
{txt} 18{com}. 
. gen treiman = treiman2 if IFAUkod==2+24
{txt} 19{com}. foreach c of numlist 2(1)29 {c -(}
{txt} 20{com}. replace treiman = treiman`c' if IFAUkod==`c'+24
{txt} 21{com}. {c )-} 
{txt} 22{com}. 
. gen educ = educ2 if IFAUkod==2+24
{txt} 23{com}. foreach z of numlist 2(1)29 {c -(}
{txt} 24{com}. replace educ = educ`z' if IFAUkod==`z'+24
{txt} 25{com}. {c )-} 
{txt} 26{com}. 
. gen inc = inc2 if IFAUkod==2+24
{txt} 27{com}. foreach x of numlist 2(1)29 {c -(}
{txt} 28{com}. replace inc = inc`x' if IFAUkod==`x'+24
{txt} 29{com}. {c )-} 
{txt} 30{com}. 
. *Drop the original country variable
. foreach d of numlist 1(1)29 {c -(}
{txt} 31{com}. drop country`d'
{txt} 32{com}. drop voted`d'
{txt} 33{com}. drop right`d'
{txt} 34{com}. drop treiman`d'
{txt} 35{com}. drop inc`d'
{txt} 36{com}. drop educ`d'
{txt} 37{com}. drop im`d'
{txt} 38{com}. {c )-} 
{txt} 39{com}. 
. 
. duplicates drop LopNr, force /*1 dublett 89*/
{txt} 40{com}. save  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'", replace
{txt} 41{com}. {c )-}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           1,760
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,760{txt}  (_merge==2)

{col 5}matched{col 30}{res}          17,139{txt}  (_merge==3)
{col 5}{hline 41}
(1,760 observations deleted)
(17,139 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(14 real changes made)
(508 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(287 real changes made)
(2,445 real changes made)
(778 real changes made)
(1,145 real changes made)
(1,509 real changes made)
(479 real changes made)
(6,727 real changes made)
(385 real changes made)
(906 real changes made)
(664 real changes made)
(552 real changes made)
(740 real changes made)
(0 real changes made)
(0 real changes made)
(17,139 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(14 real changes made)
(508 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(287 real changes made)
(2,445 real changes made)
(778 real changes made)
(1,145 real changes made)
(1,509 real changes made)
(479 real changes made)
(6,727 real changes made)
(385 real changes made)
(906 real changes made)
(664 real changes made)
(552 real changes made)
(740 real changes made)
(0 real changes made)
(0 real changes made)
(17,139 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(14 real changes made)
(508 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(287 real changes made)
(2,445 real changes made)
(778 real changes made)
(1,145 real changes made)
(1,509 real changes made)
(479 real changes made)
(6,727 real changes made)
(385 real changes made)
(906 real changes made)
(664 real changes made)
(552 real changes made)
(740 real changes made)
(0 real changes made)
(0 real changes made)
(17,139 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(11 real changes made)
(498 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(129 real changes made)
(1,946 real changes made)
(655 real changes made)
(684 real changes made)
(1,367 real changes made)
(407 real changes made)
(4,223 real changes made)
(264 real changes made)
(852 real changes made)
(551 real changes made)
(488 real changes made)
(588 real changes made)
(0 real changes made)
(0 real changes made)
(17,139 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(14 real changes made)
(508 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(287 real changes made)
(2,445 real changes made)
(778 real changes made)
(1,145 real changes made)
(1,509 real changes made)
(479 real changes made)
(6,727 real changes made)
(385 real changes made)
(906 real changes made)
(664 real changes made)
(552 real changes made)
(740 real changes made)
(0 real changes made)
(0 real changes made)
(17,139 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(13 real changes made)
(501 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(199 real changes made)
(2,303 real changes made)
(748 real changes made)
(961 real changes made)
(1,431 real changes made)
(444 real changes made)
(5,774 real changes made)
(321 real changes made)
(883 real changes made)
(624 real changes made)
(531 real changes made)
(704 real changes made)
(0 real changes made)
(0 real changes made)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp87.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           1,740
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,740{txt}  (_merge==2)

{col 5}matched{col 30}{res}          19,971{txt}  (_merge==3)
{col 5}{hline 41}
(1,740 observations deleted)
(19,971 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(9 real changes made)
(637 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(426 real changes made)
(2,566 real changes made)
(680 real changes made)
(1,464 real changes made)
(2,286 real changes made)
(439 real changes made)
(7,239 real changes made)
(1,157 real changes made)
(757 real changes made)
(731 real changes made)
(938 real changes made)
(642 real changes made)
(0 real changes made)
(0 real changes made)
(19,971 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(9 real changes made)
(637 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(426 real changes made)
(2,566 real changes made)
(680 real changes made)
(1,464 real changes made)
(2,286 real changes made)
(439 real changes made)
(7,239 real changes made)
(1,157 real changes made)
(757 real changes made)
(731 real changes made)
(938 real changes made)
(642 real changes made)
(0 real changes made)
(0 real changes made)
(19,971 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(9 real changes made)
(637 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(426 real changes made)
(2,566 real changes made)
(680 real changes made)
(1,464 real changes made)
(2,286 real changes made)
(439 real changes made)
(7,239 real changes made)
(1,157 real changes made)
(757 real changes made)
(731 real changes made)
(938 real changes made)
(642 real changes made)
(0 real changes made)
(0 real changes made)
(19,971 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(8 real changes made)
(628 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(155 real changes made)
(1,872 real changes made)
(561 real changes made)
(693 real changes made)
(1,878 real changes made)
(347 real changes made)
(4,237 real changes made)
(564 real changes made)
(696 real changes made)
(655 real changes made)
(831 real changes made)
(497 real changes made)
(0 real changes made)
(0 real changes made)
(19,971 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(9 real changes made)
(637 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(426 real changes made)
(2,566 real changes made)
(680 real changes made)
(1,464 real changes made)
(2,286 real changes made)
(439 real changes made)
(7,239 real changes made)
(1,157 real changes made)
(757 real changes made)
(731 real changes made)
(938 real changes made)
(642 real changes made)
(0 real changes made)
(0 real changes made)
(19,971 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(9 real changes made)
(630 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(313 real changes made)
(2,308 real changes made)
(641 real changes made)
(1,125 real changes made)
(2,093 real changes made)
(383 real changes made)
(5,885 real changes made)
(829 real changes made)
(706 real changes made)
(709 real changes made)
(910 real changes made)
(616 real changes made)
(0 real changes made)
(0 real changes made)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp88.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           1,701
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,701{txt}  (_merge==2)

{col 5}matched{col 30}{res}          25,886{txt}  (_merge==3)
{col 5}{hline 41}
(1,701 observations deleted)
(25,886 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(21 real changes made)
(1,043 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(369 real changes made)
(5,438 real changes made)
(808 real changes made)
(1,832 real changes made)
(5,125 real changes made)
(541 real changes made)
(5,979 real changes made)
(1,154 real changes made)
(1,158 real changes made)
(689 real changes made)
(915 real changes made)
(814 real changes made)
(0 real changes made)
(0 real changes made)
(25,886 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(21 real changes made)
(1,043 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(369 real changes made)
(5,438 real changes made)
(808 real changes made)
(1,832 real changes made)
(5,125 real changes made)
(541 real changes made)
(5,979 real changes made)
(1,154 real changes made)
(1,158 real changes made)
(689 real changes made)
(915 real changes made)
(814 real changes made)
(0 real changes made)
(0 real changes made)
(25,886 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(21 real changes made)
(1,043 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(369 real changes made)
(5,438 real changes made)
(808 real changes made)
(1,832 real changes made)
(5,125 real changes made)
(541 real changes made)
(5,979 real changes made)
(1,154 real changes made)
(1,158 real changes made)
(689 real changes made)
(915 real changes made)
(814 real changes made)
(0 real changes made)
(0 real changes made)
(25,886 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(11 real changes made)
(1,008 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(141 real changes made)
(3,671 real changes made)
(650 real changes made)
(645 real changes made)
(3,998 real changes made)
(422 real changes made)
(3,474 real changes made)
(560 real changes made)
(1,026 real changes made)
(636 real changes made)
(749 real changes made)
(639 real changes made)
(0 real changes made)
(0 real changes made)
(25,886 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(21 real changes made)
(1,043 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(369 real changes made)
(5,438 real changes made)
(808 real changes made)
(1,832 real changes made)
(5,125 real changes made)
(541 real changes made)
(5,979 real changes made)
(1,154 real changes made)
(1,158 real changes made)
(689 real changes made)
(915 real changes made)
(814 real changes made)
(0 real changes made)
(0 real changes made)
(25,886 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(13 real changes made)
(1,014 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(289 real changes made)
(4,724 real changes made)
(753 real changes made)
(1,210 real changes made)
(4,509 real changes made)
(459 real changes made)
(5,072 real changes made)
(779 real changes made)
(1,058 real changes made)
(675 real changes made)
(879 real changes made)
(775 real changes made)
(0 real changes made)
(0 real changes made)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp89.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           1,681
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,681{txt}  (_merge==2)

{col 5}matched{col 30}{res}          21,676{txt}  (_merge==3)
{col 5}{hline 41}
(1,681 observations deleted)
(21,676 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(18 real changes made)
(1,513 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(528 real changes made)
(1,370 real changes made)
(934 real changes made)
(2,329 real changes made)
(4,778 real changes made)
(550 real changes made)
(3,573 real changes made)
(1,759 real changes made)
(1,107 real changes made)
(792 real changes made)
(1,572 real changes made)
(853 real changes made)
(0 real changes made)
(0 real changes made)
(21,676 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(18 real changes made)
(1,513 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(528 real changes made)
(1,370 real changes made)
(934 real changes made)
(2,329 real changes made)
(4,778 real changes made)
(550 real changes made)
(3,573 real changes made)
(1,759 real changes made)
(1,107 real changes made)
(792 real changes made)
(1,572 real changes made)
(853 real changes made)
(0 real changes made)
(0 real changes made)
(21,676 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(18 real changes made)
(1,513 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(528 real changes made)
(1,370 real changes made)
(934 real changes made)
(2,329 real changes made)
(4,778 real changes made)
(550 real changes made)
(3,573 real changes made)
(1,759 real changes made)
(1,107 real changes made)
(792 real changes made)
(1,572 real changes made)
(853 real changes made)
(0 real changes made)
(0 real changes made)
(21,676 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(16 real changes made)
(1,433 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(431 real changes made)
(1,347 real changes made)
(831 real changes made)
(1,970 real changes made)
(4,465 real changes made)
(528 real changes made)
(3,405 real changes made)
(1,329 real changes made)
(987 real changes made)
(743 real changes made)
(1,441 real changes made)
(755 real changes made)
(0 real changes made)
(0 real changes made)
(21,676 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(18 real changes made)
(1,513 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(528 real changes made)
(1,370 real changes made)
(934 real changes made)
(2,329 real changes made)
(4,778 real changes made)
(550 real changes made)
(3,573 real changes made)
(1,759 real changes made)
(1,107 real changes made)
(792 real changes made)
(1,572 real changes made)
(853 real changes made)
(0 real changes made)
(0 real changes made)
(21,676 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(18 real changes made)
(1,508 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(528 real changes made)
(1,370 real changes made)
(926 real changes made)
(2,326 real changes made)
(4,771 real changes made)
(549 real changes made)
(3,569 real changes made)
(1,751 real changes made)
(1,099 real changes made)
(792 real changes made)
(1,561 real changes made)
(852 real changes made)
(0 real changes made)
(0 real changes made)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp90.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}           1,666
{txt}{col 9}from master{col 30}{res}               0{txt}  (_merge==1)
{col 9}from using{col 30}{res}           1,666{txt}  (_merge==2)

{col 5}matched{col 30}{res}          21,195{txt}  (_merge==3)
{col 5}{hline 41}
(1,666 observations deleted)
(21,195 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(12 real changes made)
(1,465 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(444 real changes made)
(384 real changes made)
(1,079 real changes made)
(3,284 real changes made)
(4,288 real changes made)
(714 real changes made)
(3,176 real changes made)
(1,904 real changes made)
(1,052 real changes made)
(767 real changes made)
(1,671 real changes made)
(955 real changes made)
(0 real changes made)
(0 real changes made)
(21,195 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(12 real changes made)
(1,465 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(444 real changes made)
(384 real changes made)
(1,079 real changes made)
(3,284 real changes made)
(4,288 real changes made)
(714 real changes made)
(3,176 real changes made)
(1,904 real changes made)
(1,052 real changes made)
(767 real changes made)
(1,671 real changes made)
(955 real changes made)
(0 real changes made)
(0 real changes made)
(21,195 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(12 real changes made)
(1,465 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(444 real changes made)
(384 real changes made)
(1,079 real changes made)
(3,284 real changes made)
(4,288 real changes made)
(714 real changes made)
(3,176 real changes made)
(1,904 real changes made)
(1,052 real changes made)
(767 real changes made)
(1,671 real changes made)
(955 real changes made)
(0 real changes made)
(0 real changes made)
(21,195 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(8 real changes made)
(1,309 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(354 real changes made)
(373 real changes made)
(946 real changes made)
(2,277 real changes made)
(3,900 real changes made)
(622 real changes made)
(2,970 real changes made)
(1,486 real changes made)
(895 real changes made)
(730 real changes made)
(1,521 real changes made)
(822 real changes made)
(0 real changes made)
(0 real changes made)
(21,195 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(12 real changes made)
(1,465 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(444 real changes made)
(384 real changes made)
(1,079 real changes made)
(3,284 real changes made)
(4,288 real changes made)
(714 real changes made)
(3,176 real changes made)
(1,904 real changes made)
(1,052 real changes made)
(767 real changes made)
(1,671 real changes made)
(955 real changes made)
(0 real changes made)
(0 real changes made)
(21,195 missing values generated)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(10 real changes made)
(1,338 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(0 real changes made)
(406 real changes made)
(382 real changes made)
(1,039 real changes made)
(2,890 real changes made)
(4,136 real changes made)
(675 real changes made)
(3,107 real changes made)
(1,712 real changes made)
(982 real changes made)
(763 real changes made)
(1,630 real changes made)
(943 real changes made)
(0 real changes made)
(0 real changes made)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}

{txt}(0 observations are duplicates)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp91.dta saved

{com}. 
. ///Merge with moving data
> 
. foreach y in 88 89 90 91 {c -(}
{txt}  2{com}. use "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'"
{txt}  3{com}. gen sample = 1
{txt}  4{com}. merge 1:1 LopNr using "E:\ProjData\Ethnic Enclaves, political participation\temp\movers_`y'"
{txt}  5{com}. keep if sample==1
{txt}  6{com}. drop sample _merge
{txt}  7{com}. 
. 
. save "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'", replace
{txt}  8{com}. {c )-}
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}          20,626
{txt}{col 9}from master{col 30}{res}          13,252{txt}  (_merge==1)
{col 9}from using{col 30}{res}           7,374{txt}  (_merge==2)

{col 5}matched{col 30}{res}           6,719{txt}  (_merge==3)
{col 5}{hline 41}
(7,374 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp88.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}          25,064
{txt}{col 9}from master{col 30}{res}          15,147{txt}  (_merge==1)
{col 9}from using{col 30}{res}           9,917{txt}  (_merge==2)

{col 5}matched{col 30}{res}          10,739{txt}  (_merge==3)
{col 5}{hline 41}
(9,917 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp89.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}          21,889
{txt}{col 9}from master{col 30}{res}          12,676{txt}  (_merge==1)
{col 9}from using{col 30}{res}           9,213{txt}  (_merge==2)

{col 5}matched{col 30}{res}           9,000{txt}  (_merge==3)
{col 5}{hline 41}
(9,213 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp90.dta saved
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}          19,342
{txt}{col 9}from master{col 30}{res}          13,498{txt}  (_merge==1)
{col 9}from using{col 30}{res}           5,844{txt}  (_merge==2)

{col 5}matched{col 30}{res}           7,697{txt}  (_merge==3)
{col 5}{hline 41}
(5,844 observations deleted)
file E:\ProjData\Ethnic Enclaves, political participation\temp\temp91.dta saved

{com}. 
. ///Now Create Panel
> 
. use  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp87"
{txt}
{com}. sort LopNr year
{txt}
{com}. foreach y in 88 89 90 91 {c -(}
{txt}  2{com}. append using  "E:\ProjData\Ethnic Enclaves, political participation\temp\temp`y'"
{txt}  3{com}. sort LopNr year
{txt}  4{com}. xtset LopNr year
{txt}  5{com}. {c )-}
{txt}(label IFAUkod already defined)
{res}{txt}{col 8}panel variable:  {res}LopNr (unbalanced)
{txt}{col 9}time variable:  {res}{col 25}year, 1987 to 1988
{txt}{col 17}delta:  {res}1 unit
{txt}(label IFAUkod already defined)
{res}{txt}{col 8}panel variable:  {res}LopNr (unbalanced)
{txt}{col 9}time variable:  {res}{col 25}year, 1987 to 1989, but with gaps
{txt}{col 17}delta:  {res}1 unit
{txt}(label IFAUkod already defined)
{res}{txt}{col 8}panel variable:  {res}LopNr (unbalanced)
{txt}{col 9}time variable:  {res}{col 25}year, 1987 to 1990, but with gaps
{txt}{col 17}delta:  {res}1 unit
{txt}{p 0 7 2}
(note: variable
AldPens was 
int, now long to accommodate using data's values)
{p_end}
{p 0 7 2}
(note: variable
AntAns was 
byte, now int to accommodate using data's values)
{p_end}
(label IFAUkod already defined)
{res}{txt}{col 8}panel variable:  {res}LopNr (unbalanced)
{txt}{col 9}time variable:  {res}{col 25}year, 1987 to 1991, but with gaps
{txt}{col 17}delta:  {res}1 unit
{txt}
{com}. 
. *drop a few duplicates who are in more than one year
. duplicates tag LopNr, gen(tag)

{p 0 4}{txt}Duplicates in terms of {res} LopNr{p_end}
{txt}
{com}. drop if tag==1
{txt}(248 observations deleted)

{com}. drop tag
{txt}
{com}. 
. 
. ///merge on the election data from 1994
> 
. merge 1:m LopNr using "VD\Valdelt_1994rkl"
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,152,085
{txt}{col 9}from master{col 30}{res}          41,821{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,110,264{txt}  (_merge==2)

{col 5}matched{col 30}{res}          63,798{txt}  (_merge==3)
{col 5}{hline 41}

{com}. *Keep only those left in the data in the election 1994, others we cannot analyse
. keep if _merge==3
{txt}(6,152,085 observations deleted)

{com}. drop _merge
{txt}
{com}. 
. ///merge on data on radical right party voting on municipal level from 1991
> merge m:1 Kommun using "E:\ProjData\Ethnic Enclaves, political participation\temp\nd91",
{res}{txt}{p 0 7 2}
(note: variable
Kommun was 
int, now float to accommodate using data's values)
{p_end}

{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}               0
{txt}{col 5}matched{col 30}{res}          63,798{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop _merge
{txt}
{com}. 
. ///drop ind who do not have the right to vote in municiapal elections
> ///will throw out those who were young when they immigrated to be able to vote
> 
. drop if k==3
{txt}(425 observations deleted)

{com}. 
. ///now create random order that can be used do divide into quartiles - used once!
> set seed 1
{txt}
{com}. generate order = uniform()
{txt}
{com}. 
. save  "E:\ProjData\Ethnic Enclaves, political participation\use\ind_panel", replace
{txt}file E:\ProjData\Ethnic Enclaves, political participation\use\ind_panel.dta saved

{com}. 
. ///if one wants to use the order we use: save only the order and the LopNr
> 
. keep LopNr order
{txt}
{com}. save  "E:\ProjData\Ethnic Enclaves, political participation\use\order", replace
{txt}file E:\ProjData\Ethnic Enclaves, political participation\use\order.dta saved

{com}. 
. *log close
. 
{txt}end of do-file

{com}. 
. 
. ***********
. *ANALYSIS *
. ***********
. 
. ///DESCRIPTIVES
> 
. do "C:\Userdata\Shared\Dofiles\DoAnalysis\Ethnic Enclaves, political participation\descriptives"
{txt}
{com}. ***********************************
. * SOME DESCRIPTIVES OF THE DATA *
. ***********************************
. 
. 
. ///SET DIRECTORY/log-files/set more off
> 
. clear all
{res}{txt}
{com}. cd "C:\Userdata\Shared"
{res}C:\Userdata\Shared
{txt}
{com}. set more off
{txt}
{com}. 
. *capture log close
. *log using "Logs\Ethnic Enclaves, political participation\log_descriptives", replace
. 
. 
. use "E:\ProjData\Ethnic Enclaves, political participation\use\ind_panel"
{txt}
{com}. 
. 
. 
. ///PREPARE CONTROL VARIABLES
> *age
. label variable age "Age"
{txt}
{com}. gen age2 = age^2
{txt}
{com}. label variable age2  "Age$^2$"
{txt}
{com}. 
. *sex
. label variable sex "Men"
{txt}
{com}. 
. *Married: 
. gen married = 1 if Civil==2 | Civil ==3 | Civil ==7
{txt}(29,918 missing values generated)

{com}. replace married =0 if married ==.
{txt}(29,918 real changes made)

{com}. 
. label variable married "Married"
{txt}
{com}. 
. ///Children or not
> gen parent =1 if Barn0_3>0 | Barn4_6>0 | Barn7_10>0 | Barn11_15> 0 | Barn16_17>0
{txt}(35,742 missing values generated)

{com}. replace parent =0 if parent==.
{txt}(35,742 real changes made)

{com}. replace parent =. if Barn0_3==.
{txt}(2,218 real changes made, 2,218 to missing)

{com}. label variable parent "Parent"
{txt}
{com}. 
. ///Number of kids
> gen children = Barn0_3 + Barn4_6 + Barn7_10 + Barn11_15 + Barn16_17
{txt}(2,218 missing values generated)

{com}. label variable children  "$\#$ of children"
{txt}
{com}. 
. ///Education
> encode Sun2000niva_old, gen(Sun2000niva_old_dstr)
{txt}
{com}. gen high_educ = 1 if Sun2000niva_old_dstr>4
{txt}(47,102 missing values generated)

{com}. replace high_educ =0 if high_educ==.
{txt}(47,102 real changes made)

{com}. replace high_educ=. if Sun2000niva_old_dstr==.
{txt}(2,218 real changes made, 2,218 to missing)

{com}. 
. label variable high_educ  "Highly educated"
{txt}
{com}. 
. 
. ///global macro for covariates in all regressions
> global cov sex age age2 married parent children high_educ
{txt}
{com}. 
. 
. ///INDEPENDET VARIABLE (ENCLAVE VARIABLES): coethnics
> *number of coethnics
. *drop the person him/herself
. replace coethnics = coethnics-1
{txt}(63,373 real changes made)

{com}. label variable coethnics  "$\#$ Coethnics"
{txt}
{com}. egen stdcoethnics = std(coethnics)
{txt}
{com}. label variable stdcoethnics "std number of coethnics \\ (mean =0, std=1)"
{txt}
{com}. 
. *share of population
. gen coshare = coethnics/pop
{txt}
{com}. label variable coshare "Coethnics/parish pop"
{txt}
{com}. egen stdcoshare = std(coshare)
{txt}
{com}. label variable stdcoshare  "Coethnics/ \\parish inhabitants"
{txt}
{com}. 
. 
. ///voted coethnics as share of right to vote 1982
> gen voted_coethnics = voted/right
{txt}(20,139 missing values generated)

{com}. replace voted_coethnics=0 if voted_coethnics==.
{txt}(20,139 real changes made)

{com}. egen stdvoted_coethnics = std(voted_coethnics)
{txt}
{com}. label variable voted_coethnics "Share 1982 voters among coethnics \\ with right to vote 82"
{txt}
{com}. label variable stdvoted_coethnics "Std share 1982 voters among coethnics \\ with right to vote 82"
{txt}
{com}. 
. ///voted 1982 as share of coethnics
> gen voted_coethnics2 = voted/coethnics
{txt}(420 missing values generated)

{com}. egen stdvoted_coethnics2 = std(voted_coethnics2)
{txt}(420 missing values generated)

{com}. label variable voted_coethnics2 "Share 1982 voters among all coethnics"
{txt}
{com}. label variable stdvoted_coethnics "Std share 1982 voters \\among all coethnics"
{txt}
{com}. 
. *voted 1982 as share of population
. gen sharevoted = voted/pop
{txt}
{com}. egen stdsharevoted = std(sharevoted)
{txt}
{com}. label variable stdsharevoted  "coethnics voting in 1982 \\election/parish inhabitants"
{txt}
{com}. 
. 
. ////OUTCOME VARIABLE!
> 
. ///voted or not
> gen voted_k =0 if k==1
{txt}(32,494 missing values generated)

{com}. replace voted_k=1 if voted_k==.
{txt}(32,494 real changes made)

{com}. 
. label variable voted_k  "Voted in local election"
{txt}
{com}. 
. gen voted_r = 0 if r==1
{txt}(57,925 missing values generated)

{com}. replace voted_r=1 if r==2  | r==4 | r==5
{txt}(14,144 real changes made)

{com}. replace voted_r=. if r==3
{txt}(0 real changes made)

{com}. 
. label variable voted_r  "Voted in national election"
{txt}
{com}. 
. *Labels for different types of voting
. replace k = 3 if k==4 | k==5
{txt}(9,435 real changes made)

{com}. 
. lab def k 1 "no vote",
{txt}
{com}. lab def k 2 "voted: at polling station", add
{txt}
{com}. lab def k 3 "voted: using post", add
{txt}
{com}. 
. lab values k k  
{txt}
{com}. 
. tab k, sort

 {txt}Symbol in election roll, {c |}
       Municipal election {c |}      Freq.     Percent        Cum.
{hline 26}{c +}{hline 35}
                  no vote {c |}{res}     30,879       48.73       48.73
{txt}voted: at polling station {c |}{res}     23,059       36.39       85.11
{txt}        voted: using post {c |}{res}      9,435       14.89      100.00
{txt}{hline 26}{c +}{hline 35}
                    Total {c |}{res}     63,373      100.00
{txt}
{com}. 
. 
. ///OTHER CONTROLS:
> ///moving
> *Moving variables: 
. replace move_out_muni=0 if move_out_muni==.
{txt}(49,613 real changes made)

{com}. replace move=0 if move==.
{txt}(55,486 real changes made)

{com}. label variable move_out_muni "moved out of municipality arrival year"
{txt}
{com}. 
. 
. ****************
. * DESCRIPTIONS *
. ****************
. 
. 
. ///Frquency of birth countries
> bysort year: tab IFAUkod, sort

{txt}{hline}
-> year = 1987

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                      Iran {c |}{res}      4,665       42.82       42.82
{txt}                     Chile {c |}{res}      1,511       13.87       56.69
{txt}                      Mena {c |}{res}      1,007        9.24       65.93
{txt}               East Africa {c |}{res}        808        7.42       73.35
{txt}                    Turkey {c |}{res}        601        5.52       78.86
{txt}                Yugoslavia {c |}{res}        348        3.19       82.06
{txt}                South Asia {c |}{res}        341        3.13       85.19
{txt}           South East Asia {c |}{res}        336        3.08       88.27
{txt}             South America {c |}{res}        327        3.00       91.27
{txt}                      Iraq {c |}{res}        298        2.74       94.01
{txt}              Other Africa {c |}{res}        277        2.54       96.55
{txt}                 East Asia {c |}{res}        204        1.87       98.42
{txt}           Central America {c |}{res}        161        1.48       99.90
{txt}                    Bosnia {c |}{res}         11        0.10      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     10,895      100.00

{txt}{hline}
-> year = 1988

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                      Iran {c |}{res}      4,946       39.05       39.05
{txt}                     Chile {c |}{res}      1,565       12.36       51.41
{txt}                      Mena {c |}{res}      1,369       10.81       62.22
{txt}               East Africa {c |}{res}      1,117        8.82       71.04
{txt}                      Iraq {c |}{res}        863        6.81       77.85
{txt}           South East Asia {c |}{res}        568        4.48       82.34
{txt}                    Turkey {c |}{res}        530        4.18       86.52
{txt}                Yugoslavia {c |}{res}        416        3.28       89.81
{txt}                 East Asia {c |}{res}        304        2.40       92.21
{txt}             South America {c |}{res}        269        2.12       94.33
{txt}              Other Africa {c |}{res}        245        1.93       96.27
{txt}                South Asia {c |}{res}        244        1.93       98.19
{txt}           Central America {c |}{res}        226        1.78       99.98
{txt}                    Bosnia {c |}{res}          3        0.02      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     12,665      100.00

{txt}{hline}
-> year = 1989

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                      Iran {c |}{res}      3,963       23.92       23.92
{txt}                     Chile {c |}{res}      3,345       20.19       44.11
{txt}                      Mena {c |}{res}      3,289       19.85       63.96
{txt}               East Africa {c |}{res}      1,388        8.38       72.33
{txt}                      Iraq {c |}{res}        827        4.99       77.33
{txt}                    Turkey {c |}{res}        785        4.74       82.06
{txt}                Yugoslavia {c |}{res}        673        4.06       86.12
{txt}           South East Asia {c |}{res}        569        3.43       89.56
{txt}                South Asia {c |}{res}        455        2.75       92.30
{txt}             South America {c |}{res}        388        2.34       94.65
{txt}              Other Africa {c |}{res}        344        2.08       96.72
{txt}                 East Asia {c |}{res}        337        2.03       98.76
{txt}           Central America {c |}{res}        192        1.16       99.92
{txt}                    Bosnia {c |}{res}         14        0.08      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     16,569      100.00

{txt}{hline}
-> year = 1990

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                      Mena {c |}{res}      2,762       21.40       21.40
{txt}                      Iran {c |}{res}      2,270       17.58       38.98
{txt}               East Africa {c |}{res}      1,608       12.46       51.44
{txt}                      Iraq {c |}{res}      1,240        9.61       61.04
{txt}           South East Asia {c |}{res}        907        7.03       68.07
{txt}                Yugoslavia {c |}{res}        897        6.95       75.02
{txt}                     Chile {c |}{res}        796        6.17       81.18
{txt}                    Turkey {c |}{res}        612        4.74       85.92
{txt}                South Asia {c |}{res}        459        3.56       89.48
{txt}             South America {c |}{res}        416        3.22       92.70
{txt}                 East Asia {c |}{res}        411        3.18       95.89
{txt}              Other Africa {c |}{res}        262        2.03       97.92
{txt}           Central America {c |}{res}        261        2.02       99.94
{txt}                    Bosnia {c |}{res}          8        0.06      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     12,909      100.00

{txt}{hline}
-> year = 1991

    Country of birth, IFAU {c |}
                  grouping {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
                      Mena {c |}{res}      1,905       18.43       18.43
{txt}               East Africa {c |}{res}      1,751       16.94       35.37
{txt}                      Iran {c |}{res}      1,504       14.55       49.93
{txt}                      Iraq {c |}{res}      1,190       11.51       61.44
{txt}           South East Asia {c |}{res}        834        8.07       69.51
{txt}                Yugoslavia {c |}{res}        606        5.86       75.37
{txt}                    Turkey {c |}{res}        502        4.86       80.23
{txt}                South Asia {c |}{res}        491        4.75       84.98
{txt}             South America {c |}{res}        438        4.24       89.22
{txt}                 East Asia {c |}{res}        341        3.30       92.52
{txt}              Other Africa {c |}{res}        336        3.25       95.77
{txt}                     Chile {c |}{res}        235        2.27       98.05
{txt}           Central America {c |}{res}        192        1.86       99.90
{txt}                    Bosnia {c |}{res}         10        0.10      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}     10,335      100.00

{txt}
{com}. 
. *Output
. eststo clear
{txt}
{com}. eststo: estpost tab IFAUkod if year==1987

{txt}{space 0}{space 0}{ralign 12:IFAUkod}{space 1}{c |}{space 1}{ralign 9:e(b)}{space 1}{space 1}{ralign 9:e(pct)}{space 1}{space 1}{ralign 9:e(cumpct)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Bosnia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       11}}}{space 1}{space 1}{ralign 9:{res:{sf: .1009637}}}{space 1}{space 1}{ralign 9:{res:{sf: .1009637}}}{space 1}
{space 0}{space 0}{ralign 12:Yugoslavia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      348}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.194126}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.295089}}}{space 1}
{space 0}{space 0}{ralign 12:Central_Am~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      161}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.477742}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.772832}}}{space 1}
{space 0}{space 0}{ralign 12:Chile}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1511}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.86875}}}{space 1}{space 1}{ralign 9:{res:{sf: 18.64158}}}{space 1}
{space 0}{space 0}{ralign 12:South_Amer~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      327}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.001377}}}{space 1}{space 1}{ralign 9:{res:{sf: 21.64296}}}{space 1}
{space 0}{space 0}{ralign 12:East_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      808}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.416246}}}{space 1}{space 1}{ralign 9:{res:{sf:  29.0592}}}{space 1}
{space 0}{space 0}{ralign 12:Mena}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1007}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.242772}}}{space 1}{space 1}{ralign 9:{res:{sf: 38.30197}}}{space 1}
{space 0}{space 0}{ralign 12:Other_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      277}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.542451}}}{space 1}{space 1}{ralign 9:{res:{sf: 40.84442}}}{space 1}
{space 0}{space 0}{ralign 12:Iran}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     4665}}}{space 1}{space 1}{ralign 9:{res:{sf: 42.81781}}}{space 1}{space 1}{ralign 9:{res:{sf: 83.66223}}}{space 1}
{space 0}{space 0}{ralign 12:Iraq}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      298}}}{space 1}{space 1}{ralign 9:{res:{sf:   2.7352}}}{space 1}{space 1}{ralign 9:{res:{sf: 86.39743}}}{space 1}
{space 0}{space 0}{ralign 12:Turkey}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      601}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.516292}}}{space 1}{space 1}{ralign 9:{res:{sf: 91.91372}}}{space 1}
{space 0}{space 0}{ralign 12:East_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      204}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.872419}}}{space 1}{space 1}{ralign 9:{res:{sf: 93.78614}}}{space 1}
{space 0}{space 0}{ralign 12:South_East~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      336}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.083983}}}{space 1}{space 1}{ralign 9:{res:{sf: 96.87012}}}{space 1}
{space 0}{space 0}{ralign 12:South_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      341}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.129876}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Total}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    10895}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:{space 9}}}}{space 1}
({res}est1{txt} stored)

{com}. eststo: estpost tab IFAUkod if year==1988

{txt}{space 0}{space 0}{ralign 12:IFAUkod}{space 1}{c |}{space 1}{ralign 9:e(b)}{space 1}{space 1}{ralign 9:e(pct)}{space 1}{space 1}{ralign 9:e(cumpct)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Bosnia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        3}}}{space 1}{space 1}{ralign 9:{res:{sf: .0236873}}}{space 1}{space 1}{ralign 9:{res:{sf: .0236873}}}{space 1}
{space 0}{space 0}{ralign 12:Yugoslavia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      416}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.284643}}}{space 1}{space 1}{ralign 9:{res:{sf:  3.30833}}}{space 1}
{space 0}{space 0}{ralign 12:Central_Am~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      226}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.784445}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.092775}}}{space 1}
{space 0}{space 0}{ralign 12:Chile}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1565}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.35689}}}{space 1}{space 1}{ralign 9:{res:{sf: 17.44966}}}{space 1}
{space 0}{space 0}{ralign 12:South_Amer~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      269}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.123964}}}{space 1}{space 1}{ralign 9:{res:{sf: 19.57363}}}{space 1}
{space 0}{space 0}{ralign 12:East_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1117}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.819582}}}{space 1}{space 1}{ralign 9:{res:{sf: 28.39321}}}{space 1}
{space 0}{space 0}{ralign 12:Mena}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1369}}}{space 1}{space 1}{ralign 9:{res:{sf: 10.80932}}}{space 1}{space 1}{ralign 9:{res:{sf: 39.20253}}}{space 1}
{space 0}{space 0}{ralign 12:Other_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      245}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.934465}}}{space 1}{space 1}{ralign 9:{res:{sf: 41.13699}}}{space 1}
{space 0}{space 0}{ralign 12:Iran}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     4946}}}{space 1}{space 1}{ralign 9:{res:{sf: 39.05251}}}{space 1}{space 1}{ralign 9:{res:{sf:  80.1895}}}{space 1}
{space 0}{space 0}{ralign 12:Iraq}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      863}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.814054}}}{space 1}{space 1}{ralign 9:{res:{sf: 87.00355}}}{space 1}
{space 0}{space 0}{ralign 12:Turkey}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      530}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.184761}}}{space 1}{space 1}{ralign 9:{res:{sf: 91.18831}}}{space 1}
{space 0}{space 0}{ralign 12:East_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      304}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.400316}}}{space 1}{space 1}{ralign 9:{res:{sf: 93.58863}}}{space 1}
{space 0}{space 0}{ralign 12:South_East~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      568}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.484801}}}{space 1}{space 1}{ralign 9:{res:{sf: 98.07343}}}{space 1}
{space 0}{space 0}{ralign 12:South_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      244}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.926569}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Total}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    12665}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:{space 9}}}}{space 1}
({res}est2{txt} stored)

{com}. eststo: estpost tab IFAUkod if year==1989

{txt}{space 0}{space 0}{ralign 12:IFAUkod}{space 1}{c |}{space 1}{ralign 9:e(b)}{space 1}{space 1}{ralign 9:e(pct)}{space 1}{space 1}{ralign 9:e(cumpct)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Bosnia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       14}}}{space 1}{space 1}{ralign 9:{res:{sf: .0844951}}}{space 1}{space 1}{ralign 9:{res:{sf: .0844951}}}{space 1}
{space 0}{space 0}{ralign 12:Yugoslavia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      673}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.061802}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.146297}}}{space 1}
{space 0}{space 0}{ralign 12:Central_Am~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      192}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.158791}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.305088}}}{space 1}
{space 0}{space 0}{ralign 12:Chile}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     3345}}}{space 1}{space 1}{ralign 9:{res:{sf:  20.1883}}}{space 1}{space 1}{ralign 9:{res:{sf: 25.49339}}}{space 1}
{space 0}{space 0}{ralign 12:South_Amer~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      388}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.341722}}}{space 1}{space 1}{ralign 9:{res:{sf: 27.83511}}}{space 1}
{space 0}{space 0}{ralign 12:East_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1388}}}{space 1}{space 1}{ralign 9:{res:{sf:  8.37709}}}{space 1}{space 1}{ralign 9:{res:{sf:  36.2122}}}{space 1}
{space 0}{space 0}{ralign 12:Mena}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     3289}}}{space 1}{space 1}{ralign 9:{res:{sf: 19.85032}}}{space 1}{space 1}{ralign 9:{res:{sf: 56.06253}}}{space 1}
{space 0}{space 0}{ralign 12:Other_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      344}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.076166}}}{space 1}{space 1}{ralign 9:{res:{sf: 58.13869}}}{space 1}
{space 0}{space 0}{ralign 12:Iran}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     3963}}}{space 1}{space 1}{ralign 9:{res:{sf: 23.91816}}}{space 1}{space 1}{ralign 9:{res:{sf: 82.05685}}}{space 1}
{space 0}{space 0}{ralign 12:Iraq}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      827}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.991249}}}{space 1}{space 1}{ralign 9:{res:{sf:  87.0481}}}{space 1}
{space 0}{space 0}{ralign 12:Turkey}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      785}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.737763}}}{space 1}{space 1}{ralign 9:{res:{sf: 91.78587}}}{space 1}
{space 0}{space 0}{ralign 12:East_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      337}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.033919}}}{space 1}{space 1}{ralign 9:{res:{sf: 93.81978}}}{space 1}
{space 0}{space 0}{ralign 12:South_East~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      569}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.434124}}}{space 1}{space 1}{ralign 9:{res:{sf: 97.25391}}}{space 1}
{space 0}{space 0}{ralign 12:South_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      455}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.746092}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Total}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    16569}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:{space 9}}}}{space 1}
({res}est3{txt} stored)

{com}. eststo: estpost tab IFAUkod if year==1990

{txt}{space 0}{space 0}{ralign 12:IFAUkod}{space 1}{c |}{space 1}{ralign 9:e(b)}{space 1}{space 1}{ralign 9:e(pct)}{space 1}{space 1}{ralign 9:e(cumpct)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Bosnia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        8}}}{space 1}{space 1}{ralign 9:{res:{sf: .0619723}}}{space 1}{space 1}{ralign 9:{res:{sf: .0619723}}}{space 1}
{space 0}{space 0}{ralign 12:Yugoslavia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      897}}}{space 1}{space 1}{ralign 9:{res:{sf:  6.94864}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.010613}}}{space 1}
{space 0}{space 0}{ralign 12:Central_Am~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      261}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.021845}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.032458}}}{space 1}
{space 0}{space 0}{ralign 12:Chile}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      796}}}{space 1}{space 1}{ralign 9:{res:{sf: 6.166241}}}{space 1}{space 1}{ralign 9:{res:{sf:  15.1987}}}{space 1}
{space 0}{space 0}{ralign 12:South_Amer~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      416}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.222558}}}{space 1}{space 1}{ralign 9:{res:{sf: 18.42126}}}{space 1}
{space 0}{space 0}{ralign 12:East_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1608}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.45643}}}{space 1}{space 1}{ralign 9:{res:{sf: 30.87768}}}{space 1}
{space 0}{space 0}{ralign 12:Mena}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2762}}}{space 1}{space 1}{ralign 9:{res:{sf: 21.39593}}}{space 1}{space 1}{ralign 9:{res:{sf: 52.27361}}}{space 1}
{space 0}{space 0}{ralign 12:Other_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      262}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.029592}}}{space 1}{space 1}{ralign 9:{res:{sf:  54.3032}}}{space 1}
{space 0}{space 0}{ralign 12:Iran}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2270}}}{space 1}{space 1}{ralign 9:{res:{sf: 17.58463}}}{space 1}{space 1}{ralign 9:{res:{sf: 71.88783}}}{space 1}
{space 0}{space 0}{ralign 12:Iraq}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1240}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.605701}}}{space 1}{space 1}{ralign 9:{res:{sf: 81.49353}}}{space 1}
{space 0}{space 0}{ralign 12:Turkey}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      612}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.740878}}}{space 1}{space 1}{ralign 9:{res:{sf: 86.23441}}}{space 1}
{space 0}{space 0}{ralign 12:East_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      411}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.183825}}}{space 1}{space 1}{ralign 9:{res:{sf: 89.41824}}}{space 1}
{space 0}{space 0}{ralign 12:South_East~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      907}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.026106}}}{space 1}{space 1}{ralign 9:{res:{sf: 96.44434}}}{space 1}
{space 0}{space 0}{ralign 12:South_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      459}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.555659}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Total}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    12909}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:{space 9}}}}{space 1}
({res}est4{txt} stored)

{com}. eststo: estpost tab IFAUkod if year==1991

{txt}{space 0}{space 0}{ralign 12:IFAUkod}{space 1}{c |}{space 1}{ralign 9:e(b)}{space 1}{space 1}{ralign 9:e(pct)}{space 1}{space 1}{ralign 9:e(cumpct)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Bosnia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       10}}}{space 1}{space 1}{ralign 9:{res:{sf: .0967586}}}{space 1}{space 1}{ralign 9:{res:{sf: .0967586}}}{space 1}
{space 0}{space 0}{ralign 12:Yugoslavia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      606}}}{space 1}{space 1}{ralign 9:{res:{sf:  5.86357}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.960329}}}{space 1}
{space 0}{space 0}{ralign 12:Central_Am~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      192}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.857765}}}{space 1}{space 1}{ralign 9:{res:{sf: 7.818094}}}{space 1}
{space 0}{space 0}{ralign 12:Chile}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      235}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.273827}}}{space 1}{space 1}{ralign 9:{res:{sf: 10.09192}}}{space 1}
{space 0}{space 0}{ralign 12:South_Amer~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      438}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.238026}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.32995}}}{space 1}
{space 0}{space 0}{ralign 12:East_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1751}}}{space 1}{space 1}{ralign 9:{res:{sf: 16.94243}}}{space 1}{space 1}{ralign 9:{res:{sf: 31.27238}}}{space 1}
{space 0}{space 0}{ralign 12:Mena}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1905}}}{space 1}{space 1}{ralign 9:{res:{sf: 18.43251}}}{space 1}{space 1}{ralign 9:{res:{sf: 49.70489}}}{space 1}
{space 0}{space 0}{ralign 12:Other_Africa}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      336}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.251089}}}{space 1}{space 1}{ralign 9:{res:{sf: 52.95597}}}{space 1}
{space 0}{space 0}{ralign 12:Iran}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1504}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.55249}}}{space 1}{space 1}{ralign 9:{res:{sf: 67.50847}}}{space 1}
{space 0}{space 0}{ralign 12:Iraq}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1190}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.51427}}}{space 1}{space 1}{ralign 9:{res:{sf: 79.02274}}}{space 1}
{space 0}{space 0}{ralign 12:Turkey}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      502}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.857281}}}{space 1}{space 1}{ralign 9:{res:{sf: 83.88002}}}{space 1}
{space 0}{space 0}{ralign 12:East_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      341}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.299468}}}{space 1}{space 1}{ralign 9:{res:{sf: 87.17949}}}{space 1}
{space 0}{space 0}{ralign 12:South_East~a}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      834}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.069666}}}{space 1}{space 1}{ralign 9:{res:{sf: 95.24915}}}{space 1}
{space 0}{space 0}{ralign 12:South_Asia}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      491}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.750847}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Total}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    10335}}}{space 1}{space 1}{ralign 9:{res:{sf:      100}}}{space 1}{space 1}{ralign 9:{res:{sf:{space 9}}}}{space 1}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\frequency_ethnicity.tex", ///
> replace fragment nomtitles nonotes nolines compress label nonumbers nogaps 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\frequency_ethnicity.tex"'})

{com}. 
. 
. ///SUMMARY STATS
> 
. ///first basic
> eststo clear
{txt}
{com}. *an overall summary stats table
. eststo: estpost tabstat voted_k coethnics coshare voted_coethnics2 ///
> sex age married parent children high_educ move_out_muni , ///
> statistics(mean sd min max n) columns(statistics) 

{txt}Summary statistics: mean sd min max count
     for variables: voted_k coethnics coshare voted_coethnics2 sex age married parent children high_educ move_out_muni

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:voted_k}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .512742}}}{space 1}{space 1}{ralign 9:{res:{sf: .4998416}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:coethnics}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  172.731}}}{space 1}{space 1}{ralign 9:{res:{sf: 319.9902}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:coshare}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .00958}}}{space 1}{space 1}{ralign 9:{res:{sf: .0133176}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:voted_coet~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0814853}}}{space 1}{space 1}{ralign 9:{res:{sf: .1202242}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:sex}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5790163}}}{space 1}{space 1}{ralign 9:{res:{sf: .4937208}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 31.59686}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.09594}}}{space 1}{space 1}{ralign 9:{res:{sf:       10}}}{space 1}
{space 0}{space 0}{ralign 12:married}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5279062}}}{space 1}{space 1}{ralign 9:{res:{sf: .4992246}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:parent}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4155506}}}{space 1}{space 1}{ralign 9:{res:{sf: .4928207}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:children}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .9197122}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.354683}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:high_educ}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2297931}}}{space 1}{space 1}{ralign 9:{res:{sf: .4207032}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:move_out_m~i}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2171272}}}{space 1}{space 1}{ralign 9:{res:{sf: .4122932}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(count)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:voted_k}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:    63373}}}{space 1}
{space 0}{space 0}{ralign 12:coethnics}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     2657}}}{space 1}{space 1}{ralign 9:{res:{sf:    63373}}}{space 1}
{space 0}{space 0}{ralign 12:coshare}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1254019}}}{space 1}{space 1}{ralign 9:{res:{sf:    63373}}}{space 1}
{space 0}{space 0}{ralign 12:voted_coet~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:    62953}}}{space 1}
{space 0}{space 0}{ralign 12:sex}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:    63373}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      101}}}{space 1}{space 1}{ralign 9:{res:{sf:    63373}}}{space 1}
{space 0}{space 0}{ralign 12:married}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:    63373}}}{space 1}
{space 0}{space 0}{ralign 12:parent}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:    61155}}}{space 1}
{space 0}{space 0}{ralign 12:children}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       11}}}{space 1}{space 1}{ralign 9:{res:{sf:    61155}}}{space 1}
{space 0}{space 0}{ralign 12:high_educ}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:    61155}}}{space 1}
{space 0}{space 0}{ralign 12:move_out_m~i}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:    63373}}}{space 1}
({res}est1{txt} stored)

{com}. 
. ///NO FLOAT
> esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\sumstat.tex", ///
> nonumbers fragment noobs label cells("mean(fmt(2)) sd min max count(fmt(0))") ///
> refcat(voted_k " &  &  & &  & \\ \textbf{c -(}Outcome{c )-}" coethnics " &  &  & &  & \\ \textbf{c -(}Enclave information{c )-}" sex  " &  &  & &  & \\ \textbf{c -(}Controls{c )-}", nolabel) replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\sumstat.tex"'})

{com}. 
. 
. *create a variable ==1 for larger than median treatment variable
. cumul(coshare), gen(cumcoshare)
{txt}
{com}. label variable cumcoshare "cumulative function of coethnics as share of parish population"
{txt}
{com}. gen largeenclave = 1 if cumcoshare>0.5      /*Median*/
{txt}(31,686 missing values generated)

{com}. replace largeenclave=0 if largeenclave==.
{txt}(31,686 real changes made)

{com}. 
. lab def largeenclave 1 "Large Enclave (>median)"
{txt}
{com}. lab def largeenclave 0 "Small Enclave (<median)", add
{txt}
{com}. lab values largeenclave largeenclave    
{txt}
{com}. 
. lab def voted_k 1 "Voted"
{txt}
{com}. lab def voted_k 0 "Not voted", add
{txt}
{com}. lab values voted_k voted_k      
{txt}
{com}. 
. *Create table
. eststo clear
{txt}
{com}. *an overall summary stats table
. eststo: estpost tabstat voted_k coethnics coshare voted_coethnics2 ///
> sex age married parent children high_educ move_out_muni , by(largeenclave) ///
> statistics(mean sd) columns(statistics) listwise

{txt}Summary statistics: mean sd
     for variables: voted_k coethnics coshare voted_coethnics2 sex age married parent children high_educ move_out_muni
  by categories of: largeenclave

{space 0}{space 0}{ralign 12:largeenclave}{space 1}{c |}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Small Encl~)}}{c |}{space 11}{space 11}
{space 0}{space 0}{ralign 12:voted_k}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5132293}}}{space 1}{space 1}{ralign 9:{res:{sf: .4998332}}}{space 1}
{space 0}{space 0}{ralign 12:coethnics}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 47.26148}}}{space 1}{space 1}{ralign 9:{res:{sf: 45.40218}}}{space 1}
{space 0}{space 0}{ralign 12:coshare}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0026078}}}{space 1}{space 1}{ralign 9:{res:{sf: .0014115}}}{space 1}
{space 0}{space 0}{ralign 12:voted_coet~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0792853}}}{space 1}{space 1}{ralign 9:{res:{sf: .1190184}}}{space 1}
{space 0}{space 0}{ralign 12:sex}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .601733}}}{space 1}{space 1}{ralign 9:{res:{sf: .4895491}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 31.07154}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.59071}}}{space 1}
{space 0}{space 0}{ralign 12:married}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5193478}}}{space 1}{space 1}{ralign 9:{res:{sf: .4996338}}}{space 1}
{space 0}{space 0}{ralign 12:parent}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3977047}}}{space 1}{space 1}{ralign 9:{res:{sf: .4894319}}}{space 1}
{space 0}{space 0}{ralign 12:children}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .8857653}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.357746}}}{space 1}
{space 0}{space 0}{ralign 12:high_educ}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2393174}}}{space 1}{space 1}{ralign 9:{res:{sf: .4266739}}}{space 1}
{space 0}{space 0}{ralign 12:move_out_m~i}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2250298}}}{space 1}{space 1}{ralign 9:{res:{sf: .4176088}}}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Large Encl~)}}{c |}{space 11}{space 11}
{space 0}{space 0}{ralign 12:voted_k}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5159831}}}{space 1}{space 1}{ralign 9:{res:{sf: .4997527}}}{space 1}
{space 0}{space 0}{ralign 12:coethnics}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 298.2369}}}{space 1}{space 1}{ralign 9:{res:{sf: 411.7955}}}{space 1}
{space 0}{space 0}{ralign 12:coshare}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0165755}}}{space 1}{space 1}{ralign 9:{res:{sf: .0159514}}}{space 1}
{space 0}{space 0}{ralign 12:voted_coet~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0838808}}}{space 1}{space 1}{ralign 9:{res:{sf: .1219716}}}{space 1}
{space 0}{space 0}{ralign 12:sex}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5556867}}}{space 1}{space 1}{ralign 9:{res:{sf: .4968975}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 33.46202}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.93012}}}{space 1}
{space 0}{space 0}{ralign 12:married}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5767024}}}{space 1}{space 1}{ralign 9:{res:{sf: .4940898}}}{space 1}
{space 0}{space 0}{ralign 12:parent}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4365103}}}{space 1}{space 1}{ralign 9:{res:{sf: .4959608}}}{space 1}
{space 0}{space 0}{ralign 12:children}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .9616406}}}{space 1}{space 1}{ralign 9:{res:{sf:  1.35452}}}{space 1}
{space 0}{space 0}{ralign 12:high_educ}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2201239}}}{space 1}{space 1}{ralign 9:{res:{sf: .4143368}}}{space 1}
{space 0}{space 0}{ralign 12:move_out_m~i}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2113045}}}{space 1}{space 1}{ralign 9:{res:{sf: .4082406}}}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Total}}{c |}{space 11}{space 11}
{space 0}{space 0}{ralign 12:voted_k}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5146122}}}{space 1}{space 1}{ralign 9:{res:{sf: .4997906}}}{space 1}
{space 0}{space 0}{ralign 12:coethnics}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 173.2967}}}{space 1}{space 1}{ralign 9:{res:{sf: 319.2642}}}{space 1}
{space 0}{space 0}{ralign 12:coshare}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0096221}}}{space 1}{space 1}{ralign 9:{res:{sf: .0133245}}}{space 1}
{space 0}{space 0}{ralign 12:voted_coet~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0815931}}}{space 1}{space 1}{ralign 9:{res:{sf: .1205314}}}{space 1}
{space 0}{space 0}{ralign 12:sex}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5786094}}}{space 1}{space 1}{ralign 9:{res:{sf:  .493786}}}{space 1}
{space 0}{space 0}{ralign 12:age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 32.27199}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.87449}}}{space 1}
{space 0}{space 0}{ralign 12:married}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5481502}}}{space 1}{space 1}{ralign 9:{res:{sf: .4976803}}}{space 1}
{space 0}{space 0}{ralign 12:parent}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4171922}}}{space 1}{space 1}{ralign 9:{res:{sf: .4930992}}}{space 1}
{space 0}{space 0}{ralign 12:children}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .9238685}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.356646}}}{space 1}
{space 0}{space 0}{ralign 12:high_educ}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2296788}}}{space 1}{space 1}{ralign 9:{res:{sf: .4206297}}}{space 1}
{space 0}{space 0}{ralign 12:move_out_m~i}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2181372}}}{space 1}{space 1}{ralign 9:{res:{sf: .4129845}}}{space 1}
({res}est1{txt} stored)

{com}. 
. ///NO FLOAT
> esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\sumstat_byen.tex", ///
> main(mean) aux(sd) nostar unstack noobs nonote label ///
> refcat(voted_k " &  &  & &  & \\ \textbf{c -(}Outcome{c )-}" coethnics " &  &  & &  & \\ \textbf{c -(}Enclave information{c )-}" sex  " &  &  & &  & \\ \textbf{c -(}Controls{c )-}", nolabel) replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\sumstat_byen.tex"'})

{com}. 
. 
. ////for the long term
> 
. 
. rename r r_first
{res}{txt}
{com}. rename k k_first
{res}{txt}
{com}. rename l l_first
{res}{txt}
{com}.  
.  
. merge 1:m LopNr using "D:\SCB_ConPol\Stata\VD\Valdelt_2010", keepusing (LopNr r k l)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,213,376
{txt}{col 9}from master{col 30}{res}          11,355{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,202,021{txt}  (_merge==2)

{col 5}matched{col 30}{res}          52,018{txt}  (_merge==3)
{col 5}{hline 41}

{com}. keep if _merge==3
{txt}(7,213,376 observations deleted)

{com}. 
. 
. *check for duplicates - OK
. *duplicates report LopNr 
. 
. ///create voting variable
> 
. ///voted or not
> gen voted_k2010 =0 if k==1
{txt}(36,940 missing values generated)

{com}. replace voted_k2010=1 if voted_k2010==.
{txt}(36,940 real changes made)

{com}. 
. label variable voted_k2010  "voted in local election 2010"
{txt}
{com}. 
. 
. ///summary of voted 2010
> eststo clear
{txt}
{com}. eststo: estpost tabstat voted_k2010 , ///
> statistics(mean sd min max n) columns(statistics) 

{txt}Summary statistics: mean sd min max count
     for variables: voted_k2010

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:voted_k2010}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .7101388}}}{space 1}{space 1}{ralign 9:{res:{sf: .4537022}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(count)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:voted_k2010}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:    52018}}}{space 1}
({res}est1{txt} stored)

{com}. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\sumstat2010.tex", ///
> nonumbers fragment noobs label cells("mean(fmt(2)) sd min max count(fmt(0))") ///
> refcat(voted_k " &  &  & &  & \\ \textbf{c -(}Outcome{c )-}" coethnics " &  &  & &  & \\ \textbf{c -(}Enclave information{c )-}" sex  " &  &  & &  & \\ \textbf{c -(}Controls{c )-}", nolabel) replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\sumstat2010.tex"'})

{com}. 
. *an overall summary stats table
. eststo clear
{txt}
{com}. eststo: estpost tabstat voted_k2010 , by(largeenclave) ///
> statistics(mean sd) columns(statistics) listwise

{txt}Summary statistics: mean sd
     for variables: voted_k2010
  by categories of: largeenclave

{space 0}{space 0}{ralign 12:largeenclave}{space 1}{c |}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Small_Encl~)}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .7192875}}}{space 1}{space 1}{ralign 9:{res:{sf: .4493559}}}{space 1}
{space 0}{space 0}{ralign 12:Large_Encl~)}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .7009223}}}{space 1}{space 1}{ralign 9:{res:{sf: .4578628}}}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Total}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .7101388}}}{space 1}{space 1}{ralign 9:{res:{sf: .4537022}}}{space 1}
({res}est1{txt} stored)

{com}. 
. ///NO FLOAT
> esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\sumstat_byen2010.tex", ///
> main(mean) aux(sd) nostar unstack noobs nonote label ///
> refcat(voted_k " &  &  & &  & \\ \textbf{c -(}Outcome{c )-}" coethnics " &  &  & &  & \\ \textbf{c -(}Enclave information{c )-}" sex  " &  &  & &  & \\ \textbf{c -(}Controls{c )-}", nolabel) replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\sumstat_byen2010.tex"'})

{com}. 
. 
. 
. 
. *log close
. 
{txt}end of do-file

{com}. 
. ///REGRESSIONS/other analysis
> do "C:\Userdata\Shared\Dofiles\DoAnalysis\Ethnic Enclaves, political participation\reg"
{txt}
{com}. *********************
. * BASIC REGRESSIONS *
. *********************
. 
. 
. ///SET DIRECTORY/log-files/set more off
> 
. clear all
{res}{txt}
{com}. cd "C:\Userdata\Shared"
{res}C:\Userdata\Shared
{txt}
{com}. set more off
{txt}
{com}. 
. *capture log close
. *log using "Logs\Ethnic Enclaves, political participation\log_REG", replace
. 
. 
. use "E:\ProjData\Ethnic Enclaves, political participation\use\ind_panel"
{txt}
{com}. 
. 
. ///PREPARE CONTROL VARIABLES
> *age
. label variable age "Age"
{txt}
{com}. gen age2 = age^2
{txt}
{com}. label variable age2  "Age$^2$"
{txt}
{com}. 
. *sex
. label variable sex "Man"
{txt}
{com}. 
. *Married: 
. gen married = 1 if Civil==2 | Civil ==3 | Civil ==7
{txt}(29,918 missing values generated)

{com}. replace married =0 if married ==.
{txt}(29,918 real changes made)

{com}. 
. label variable married "Married"
{txt}
{com}. 
. ///Children or not
> gen parent =1 if Barn0_3>0 | Barn4_6>0 | Barn7_10>0 | Barn11_15> 0 | Barn16_17>0
{txt}(35,742 missing values generated)

{com}. replace parent =0 if parent==.
{txt}(35,742 real changes made)

{com}. replace parent =. if Barn0_3==.
{txt}(2,218 real changes made, 2,218 to missing)

{com}. label variable parent "Parent"
{txt}
{com}. 
. ///Number of kids
> gen children = Barn0_3 + Barn4_6 + Barn7_10 + Barn11_15 + Barn16_17
{txt}(2,218 missing values generated)

{com}. label variable children  "$\#$ of children"
{txt}
{com}. 
. ///Education
> encode Sun2000niva_old, gen(Sun2000niva_old_dstr)
{txt}
{com}. gen high_educ = 1 if Sun2000niva_old_dstr>4
{txt}(47,102 missing values generated)

{com}. replace high_educ =0 if high_educ==.
{txt}(47,102 real changes made)

{com}. replace high_educ=. if Sun2000niva_old_dstr==.
{txt}(2,218 real changes made, 2,218 to missing)

{com}. 
. label variable high_educ  "Highly educated"
{txt}
{com}. 
. 
. ///global macro for covariates in all regressions
> global cov sex age age2 married parent children high_educ
{txt}
{com}. 
. 
. 
. ///INDEPENDET VARIABLE (ENCLAVE VARIABLES): coethnics
> *number of coethnics
. *drop the person him/herself
. replace coethnics = coethnics-1
{txt}(63,373 real changes made)

{com}. label variable coethnics  "Coethnics"
{txt}
{com}. egen stdcoethnics = std(coethnics)
{txt}
{com}. label variable stdcoethnics "std number of coethnics \\ (mean =0, std=1)"
{txt}
{com}. 
. *share of population
. gen coshare = coethnics/pop
{txt}
{com}. egen stdcoshare = std(coshare)
{txt}
{com}. 
. label variable stdcoshare  "Coethnics/ \\parish inhabitants"
{txt}
{com}. 
. *logs
. gen logcoshare = ln(coshare)
{txt}(420 missing values generated)

{com}. gen logcoethnics = ln(coethnics)
{txt}(420 missing values generated)

{com}. 
. label variable logcoshare  "log(Coethnics/ \\parish inhabitants)"
{txt}
{com}. label variable logcoethnics  "log(Coethnics)"
{txt}
{com}. 
. 
. ///voted coethnics as share of right to vote 1982
> gen voted_coethnics = voted/right
{txt}(20,139 missing values generated)

{com}. replace voted_coethnics=0 if voted_coethnics==.
{txt}(20,139 real changes made)

{com}. egen stdvoted_coethnics = std(voted_coethnics)
{txt}
{com}. label variable voted_coethnics "Share 1982 voters among coethnics \\ with right to vote 82"
{txt}
{com}. label variable stdvoted_coethnics "Std share 1982 voters among coethnics \\ with right to vote 82"
{txt}
{com}. 
. ///NOW CREATE A CUMULATIVE DISTRIBUTION BASED ON VOTING COETHNICS
> sort voted_coethnics order
{txt}
{com}. gen N = _N
{txt}
{com}. gen CD1 = _n
{txt}
{com}. gen voted_coethnics_alt_cum = CD1/N
{txt}
{com}. 
. ///voted 1982 as share of coethnics
> gen voted_coethnics2 = voted/coethnics
{txt}(420 missing values generated)

{com}. egen stdvoted_coethnics2 = std(voted_coethnics2)
{txt}(420 missing values generated)

{com}. label variable voted_coethnics2 "Share 1982 voters \\among all coethnics"
{txt}
{com}. label variable stdvoted_coethnics "Std share 1982 voters \\among all coethnics"
{txt}
{com}. 
. ///AGAIN, CREATE A CUMULATIVE DISTRIBUTION BASED ON VOTING COETHNICS/POP
> sort voted_coethnics2 order
{txt}
{com}. gen CD2 = _n
{txt}
{com}. gen voted_coethnics_cum = CD2/N
{txt}
{com}. 
. *voted 1982 as share of population
. gen sharevoted = voted/pop
{txt}
{com}. egen stdsharevoted = std(sharevoted)
{txt}
{com}. label variable stdsharevoted  "coethnics voting in 1982 \\election/parish inhabitants"
{txt}
{com}. 
. 
. 
. 
. ///economic/educatinal 
> egen stdtreiman = std(treiman)
{txt}(14188 missing values generated)

{com}. label variable stdtreiman  "mean occupational status \\among parish coethnics"
{txt}
{com}. gen logtreiman = ln(treiman)
{txt}(14,188 missing values generated)

{com}. 
. egen stdinc = std(inc)
{txt}(5613 missing values generated)

{com}. label variable stdinc  "mean total income \\among parish coethnics"
{txt}
{com}. gen loginc = ln(inc)
{txt}(8,958 missing values generated)

{com}. 
. gen shareedu = educ/coethnics
{txt}(420 missing values generated)

{com}. egen stdedu = std(shareedu)
{txt}(420 missing values generated)

{com}. label variable stdedu  "share of high edu. \\among parish coethnics"
{txt}
{com}. 
. 
. ////OUTCOME VARIABLE!
> 
. ///voted or not
> gen voted_k =0 if k==1
{txt}(32,494 missing values generated)

{com}. replace voted_k=1 if voted_k==.
{txt}(32,494 real changes made)

{com}. 
. label variable voted_k  "voted in local election"
{txt}
{com}. 
. gen voted_r = 0 if r==1
{txt}(57,925 missing values generated)

{com}. replace voted_r=1 if r==2  | r==4 | r==5
{txt}(14,144 real changes made)

{com}. replace voted_r=. if r==3
{txt}(0 real changes made)

{com}. 
. label variable voted_r  "voted in national election"
{txt}
{com}. 
. *Labels for different types of voting
. replace k = 3 if k==4 | k==5
{txt}(9,435 real changes made)

{com}. 
. lab def k 1 "no vote",
{txt}
{com}. lab def k 2 "voted: at polling station", add
{txt}
{com}. lab def k 3 "voted: using post", add
{txt}
{com}. 
. lab values k k  
{txt}
{com}. 
. tab k, sort

 {txt}Symbol in election roll, {c |}
       Municipal election {c |}      Freq.     Percent        Cum.
{hline 26}{c +}{hline 35}
                  no vote {c |}{res}     30,879       48.73       48.73
{txt}voted: at polling station {c |}{res}     23,059       36.39       85.11
{txt}        voted: using post {c |}{res}      9,435       14.89      100.00
{txt}{hline 26}{c +}{hline 35}
                    Total {c |}{res}     63,373      100.00
{txt}
{com}. 
. *share who voted in the parish
. gen sharevoted_parish = votedk/rightk
{txt}
{com}. egen stdpartrate = std(sharevoted_parish)
{txt}
{com}. 
. label variable stdpartrate "voter participation rate parish"
{txt}
{com}. 
. 
. ///OTHER CONTROLS:
> ///moving
> *Moving variables: 
. replace move_out_muni=0 if move_out_muni==.
{txt}(49,613 real changes made)

{com}. replace move=0 if move==.
{txt}(55,486 real changes made)

{com}. label variable move_out_muni "moved out of municipality arrival year"
{txt}
{com}. 
. 
. ///cohort by country fixed effect
> egen cohortbycountry = group(IFAUkod im_year)
{txt}
{com}. 
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////
> ///Baseline reg - NOTE TABLE 2 IS CREATED MANUALLY BY COMBINING "reg" AND "reglong"
> ////////////////////////////////////////////////////////////////////////////////////
> 
. ///short run effect
> 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    61,155
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}     1,063
{txt}{col 49}F({res}  77{txt},{res}    285{txt}){col 67}= {res}    148.66
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1090
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0921
{txt}{col 49}Root MSE{col 67}= {res}    0.4762

{txt}{ralign 78:(Std. Err. adjusted for {res:286} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0075737{col 26}{space 2} .0068444{col 37}{space 1}    1.11{col 46}{space 3}0.269{col 54}{space 4}-.0058982{col 67}{space 3} .0210456
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0185757{col 26}{space 2} .0046634{col 37}{space 1}    3.98{col 46}{space 3}0.000{col 54}{space 4} .0093967{col 67}{space 3} .0277548
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0195798{col 26}{space 2} .0012746{col 37}{space 1}   15.36{col 46}{space 3}0.000{col 54}{space 4}  .017071{col 67}{space 3} .0220886
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002302{col 26}{space 2} .0000148{col 37}{space 1}  -15.55{col 46}{space 3}0.000{col 54}{space 4}-.0002594{col 67}{space 3}-.0002011
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0122084{col 26}{space 2} .0051827{col 37}{space 1}    2.36{col 46}{space 3}0.019{col 54}{space 4} .0020072{col 67}{space 3} .0224096
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0513597{col 26}{space 2} .0085769{col 37}{space 1}    5.99{col 46}{space 3}0.000{col 54}{space 4} .0344777{col 67}{space 3} .0682417
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0153576{col 26}{space 2}  .003558{col 37}{space 1}    4.32{col 46}{space 3}0.000{col 54}{space 4} .0083544{col 67}{space 3} .0223609
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0788355{col 26}{space 2} .0056577{col 37}{space 1}   13.93{col 46}{space 3}0.000{col 54}{space 4} .0676993{col 67}{space 3} .0899717
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .2321818{col 26}{space 2} .3535254{col 37}{space 1}    0.66{col 46}{space 3}0.512{col 54}{space 4}-.4636703{col 67}{space 3} .9280339
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1136983{col 26}{space 2} .2037436{col 37}{space 1}    0.56{col 46}{space 3}0.577{col 54}{space 4}-.2873348{col 67}{space 3} .5147314
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1290894{col 26}{space 2} .1454471{col 37}{space 1}    0.89{col 46}{space 3}0.376{col 54}{space 4}-.1571974{col 67}{space 3} .4153763
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1859943{col 26}{space 2} .2378376{col 37}{space 1}    0.78{col 46}{space 3}0.435{col 54}{space 4}-.2821468{col 67}{space 3} .6541354
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1797681{col 26}{space 2} .1569103{col 37}{space 1}    1.15{col 46}{space 3}0.253{col 54}{space 4} -.129082{col 67}{space 3} .4886182
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2262721{col 26}{space 2} .1368405{col 37}{space 1}    1.65{col 46}{space 3}0.099{col 54}{space 4}-.0430742{col 67}{space 3} .4956184
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1599042{col 26}{space 2} .1454741{col 37}{space 1}    1.10{col 46}{space 3}0.273{col 54}{space 4}-.1264357{col 67}{space 3} .4462441
{txt}{space 10}9  {c |}{col 14}{res}{space 2}  .123143{col 26}{space 2} .1393803{col 37}{space 1}    0.88{col 46}{space 3}0.378{col 54}{space 4}-.1512024{col 67}{space 3} .3974884
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1351323{col 26}{space 2} .1481621{col 37}{space 1}    0.91{col 46}{space 3}0.363{col 54}{space 4}-.1564985{col 67}{space 3} .4267631
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4841118{col 26}{space 2} .1523717{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4} .1841952{col 67}{space 3} .7840285
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .3951742{col 26}{space 2} .1473183{col 37}{space 1}    2.68{col 46}{space 3}0.008{col 54}{space 4} .1052042{col 67}{space 3} .6851442
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{txt}{space 9}23  {c |}{col 14}{res}{space 2} .4348857{col 26}{space 2} .1343683{col 37}{space 1}    3.24{col 46}{space 3}0.001{col 54}{space 4} .1704056{col 67}{space 3} .6993659
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{txt}{space 9}26  {c |}{col 14}{res}{space 2} .4044021{col 26}{space 2} .1304807{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4}  .147574{col 67}{space 3} .6612302
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{txt}{space 9}28  {c |}{col 14}{res}{space 2} .2979656{col 26}{space 2} .1263671{col 37}{space 1}    2.36{col 46}{space 3}0.019{col 54}{space 4} .0492344{col 67}{space 3} .5466968
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .2551678{col 26}{space 2} .1291483{col 37}{space 1}    1.98{col 46}{space 3}0.049{col 54}{space 4} .0009623{col 67}{space 3} .5093733
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{txt}{space 9}48  {c |}{col 14}{res}{space 2} .3887823{col 26}{space 2} .1296682{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .1335535{col 67}{space 3} .6440111
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .3144585{col 26}{space 2}  .134003{col 37}{space 1}    2.35{col 46}{space 3}0.020{col 54}{space 4} .0506973{col 67}{space 3} .5782197
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .2973138{col 26}{space 2} .1263052{col 37}{space 1}    2.35{col 46}{space 3}0.019{col 54}{space 4} .0487043{col 67}{space 3} .5459232
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .4704047{col 26}{space 2} .1411418{col 37}{space 1}    3.33{col 46}{space 3}0.001{col 54}{space 4}  .192592{col 67}{space 3} .7482174
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{txt}{space 9}68  {c |}{col 14}{res}{space 2}  .434078{col 26}{space 2} .1389109{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .1606565{col 67}{space 3} .7074995
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .3489918{col 26}{space 2} .1406742{col 37}{space 1}    2.48{col 46}{space 3}0.014{col 54}{space 4} .0720995{col 67}{space 3}  .625884
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .3104538{col 26}{space 2} .1440791{col 37}{space 1}    2.15{col 46}{space 3}0.032{col 54}{space 4} .0268596{col 67}{space 3}  .594048
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2393594{col 26}{space 2} .1364085{col 37}{space 1}   -1.75{col 46}{space 3}0.080{col 54}{space 4}-.5078554{col 67}{space 3} .0291366
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 6.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,332
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       825
{txt}{col 49}F({res}  73{txt},{res}    277{txt}){col 67}= {res}     20.11
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1528
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1001
{txt}{col 49}Root MSE{col 67}= {res}    0.4717

{txt}{ralign 78:(Std. Err. adjusted for {res:278} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0017762{col 26}{space 2} .0097319{col 37}{space 1}   -0.18{col 46}{space 3}0.855{col 54}{space 4}-.0209341{col 67}{space 3} .0173818
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0283976{col 26}{space 2} .0087069{col 37}{space 1}    3.26{col 46}{space 3}0.001{col 54}{space 4} .0112576{col 67}{space 3} .0455376
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0255526{col 26}{space 2} .0017992{col 37}{space 1}   14.20{col 46}{space 3}0.000{col 54}{space 4} .0220107{col 67}{space 3} .0290945
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0003093{col 26}{space 2} .0000218{col 37}{space 1}  -14.17{col 46}{space 3}0.000{col 54}{space 4}-.0003523{col 67}{space 3}-.0002663
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0265601{col 26}{space 2} .0122273{col 37}{space 1}    2.17{col 46}{space 3}0.031{col 54}{space 4} .0024899{col 67}{space 3} .0506303
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0415503{col 26}{space 2} .0164899{col 37}{space 1}    2.52{col 46}{space 3}0.012{col 54}{space 4} .0090889{col 67}{space 3} .0740117
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0136205{col 26}{space 2} .0052614{col 37}{space 1}    2.59{col 46}{space 3}0.010{col 54}{space 4} .0032632{col 67}{space 3} .0239778
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0567084{col 26}{space 2} .0106229{col 37}{space 1}    5.34{col 46}{space 3}0.000{col 54}{space 4} .0357966{col 67}{space 3} .0776202
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2} .0594866{col 26}{space 2} .3222624{col 37}{space 1}    0.18{col 46}{space 3}0.854{col 54}{space 4} -.574908{col 67}{space 3} .6938812
{txt}{space 10}6  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}7  {c |}{col 14}{res}{space 2} .5965667{col 26}{space 2} .3043267{col 37}{space 1}    1.96{col 46}{space 3}0.051{col 54}{space 4}-.0025202{col 67}{space 3} 1.195654
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1759696{col 26}{space 2} .1794533{col 37}{space 1}    0.98{col 46}{space 3}0.328{col 54}{space 4}-.1772959{col 67}{space 3} .5292352
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3262016{col 26}{space 2} .1942671{col 37}{space 1}    1.68{col 46}{space 3}0.094{col 54}{space 4}-.0562257{col 67}{space 3}  .708629
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2868489{col 26}{space 2} .1778683{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4}-.0632965{col 67}{space 3} .6369942
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .7619715{col 26}{space 2} .1705915{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .4261511{col 67}{space 3} 1.097792
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{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6252021{col 26}{space 2} .1721092{col 37}{space 1}    3.63{col 46}{space 3}0.000{col 54}{space 4}  .286394{col 67}{space 3} .9640103
{txt}{space 9}70  {c |}{col 14}{res}{space 2}  .558192{col 26}{space 2}  .162905{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} .2375029{col 67}{space 3} .8788811
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.5595407{col 26}{space 2} .1442177{col 37}{space 1}   -3.88{col 46}{space 3}0.000{col 54}{space 4}-.8434427{col 67}{space 3}-.2756386
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,261
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       754
{txt}{col 49}{help j_robustsingular##|_new:F(  73,    275)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1431
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0939
{txt}{col 49}Root MSE{col 67}= {res}    0.4753

{txt}{ralign 78:(Std. Err. adjusted for {res:276} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0098301{col 26}{space 2}  .012305{col 37}{space 1}   -0.80{col 46}{space 3}0.425{col 54}{space 4} -.034054{col 67}{space 3} .0143939
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0336607{col 26}{space 2} .0104764{col 37}{space 1}    3.21{col 46}{space 3}0.001{col 54}{space 4} .0130366{col 67}{space 3} .0542848
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .020858{col 26}{space 2} .0027434{col 37}{space 1}    7.60{col 46}{space 3}0.000{col 54}{space 4} .0154572{col 67}{space 3} .0262588
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002566{col 26}{space 2} .0000293{col 37}{space 1}   -8.76{col 46}{space 3}0.000{col 54}{space 4}-.0003143{col 67}{space 3}-.0001989
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0307608{col 26}{space 2} .0132067{col 37}{space 1}    2.33{col 46}{space 3}0.021{col 54}{space 4} .0047617{col 67}{space 3} .0567599
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0464527{col 26}{space 2} .0171559{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .0126791{col 67}{space 3} .0802264
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0134593{col 26}{space 2} .0073763{col 37}{space 1}    1.82{col 46}{space 3}0.069{col 54}{space 4}-.0010618{col 67}{space 3} .0279804
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0820193{col 26}{space 2} .0112423{col 37}{space 1}    7.30{col 46}{space 3}0.000{col 54}{space 4} .0598874{col 67}{space 3} .1041511
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.2617975{col 26}{space 2} .0849904{col 37}{space 1}   -3.08{col 46}{space 3}0.002{col 54}{space 4} -.429112{col 67}{space 3} -.094483
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0078806{col 26}{space 2} .0841205{col 37}{space 1}    0.09{col 46}{space 3}0.925{col 54}{space 4}-.1577214{col 67}{space 3} .1734827
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0752458{col 26}{space 2} .0826782{col 37}{space 1}   -0.91{col 46}{space 3}0.364{col 54}{space 4}-.2380083{col 67}{space 3} .0875168
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1580254{col 26}{space 2} .1098141{col 37}{space 1}    1.44{col 46}{space 3}0.151{col 54}{space 4}-.0581577{col 67}{space 3} .3742086
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2} .6687095{col 26}{space 2} .2276537{col 37}{space 1}    2.94{col 46}{space 3}0.004{col 54}{space 4}  .220544{col 67}{space 3} 1.116875
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3204332{col 26}{space 2} .1479133{col 37}{space 1}    2.17{col 46}{space 3}0.031{col 54}{space 4}  .029247{col 67}{space 3} .6116194
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2160283{col 26}{space 2} .1398074{col 37}{space 1}    1.55{col 46}{space 3}0.123{col 54}{space 4}-.0592005{col 67}{space 3}  .491257
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .8407085{col 26}{space 2} .1386919{col 37}{space 1}    6.06{col 46}{space 3}0.000{col 54}{space 4} .5676756{col 67}{space 3} 1.113741
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5677588{col 26}{space 2} .1043639{col 37}{space 1}    5.44{col 46}{space 3}0.000{col 54}{space 4}  .362305{col 67}{space 3} .7732126
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5659971{col 26}{space 2} .1263908{col 37}{space 1}    4.48{col 46}{space 3}0.000{col 54}{space 4} .3171805{col 67}{space 3} .8148136
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .6179413{col 26}{space 2} .0886872{col 37}{space 1}    6.97{col 46}{space 3}0.000{col 54}{space 4} .4433492{col 67}{space 3} .7925335
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .4917571{col 26}{space 2} .1492939{col 37}{space 1}    3.29{col 46}{space 3}0.001{col 54}{space 4}  .197853{col 67}{space 3} .7856612
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .6482641{col 26}{space 2} .0915374{col 37}{space 1}    7.08{col 46}{space 3}0.000{col 54}{space 4} .4680611{col 67}{space 3} .8284671
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .6272694{col 26}{space 2} .0890492{col 37}{space 1}    7.04{col 46}{space 3}0.000{col 54}{space 4} .4519647{col 67}{space 3} .8025741
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5986375{col 26}{space 2} .0897714{col 37}{space 1}    6.67{col 46}{space 3}0.000{col 54}{space 4}  .421911{col 67}{space 3}  .775364
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .5649315{col 26}{space 2} .0995589{col 37}{space 1}    5.67{col 46}{space 3}0.000{col 54}{space 4} .3689371{col 67}{space 3} .7609258
{txt}{space 9}20  {c |}{col 14}{res}{space 2}  .689639{col 26}{space 2} .1518426{col 37}{space 1}    4.54{col 46}{space 3}0.000{col 54}{space 4} .3907174{col 67}{space 3} .9885605
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .7335139{col 26}{space 2} .2114538{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4} .3172402{col 67}{space 3} 1.149788
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .7394641{col 26}{space 2} .2330092{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4} .2807557{col 67}{space 3} 1.198173
{txt}{space 9}23  {c |}{col 14}{res}{space 2}  .570631{col 26}{space 2} .1547835{col 37}{space 1}    3.69{col 46}{space 3}0.000{col 54}{space 4}   .26592{col 67}{space 3}  .875342
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .3626759{col 26}{space 2} .1357862{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4} .0953634{col 67}{space 3} .6299884
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .4071833{col 26}{space 2} .1451606{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4}  .121416{col 67}{space 3} .6929505
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .5009562{col 26}{space 2} .1004741{col 37}{space 1}    4.99{col 46}{space 3}0.000{col 54}{space 4}   .30316{col 67}{space 3} .6987523
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .5412088{col 26}{space 2} .0900176{col 37}{space 1}    6.01{col 46}{space 3}0.000{col 54}{space 4} .3639976{col 67}{space 3}   .71842
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .4554669{col 26}{space 2} .0882795{col 37}{space 1}    5.16{col 46}{space 3}0.000{col 54}{space 4} .2816775{col 67}{space 3} .6292563
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{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6188589{col 26}{space 2} .1358693{col 37}{space 1}    4.55{col 46}{space 3}0.000{col 54}{space 4} .3513827{col 67}{space 3} .8863351
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4925572{col 26}{space 2} .1272238{col 37}{space 1}    3.87{col 46}{space 3}0.000{col 54}{space 4} .2421009{col 67}{space 3} .7430135
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4224024{col 26}{space 2} .1043571{col 37}{space 1}   -4.05{col 46}{space 3}0.000{col 54}{space 4}-.6278427{col 67}{space 3}-.2169621
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,261
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       413
{txt}{col 49}F({res}  72{txt},{res}    179{txt}){col 67}= {res}  16315.42
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1241
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0954
{txt}{col 49}Root MSE{col 67}= {res}    0.4755

{txt}{ralign 78:(Std. Err. adjusted for {res:180} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} -.003687{col 26}{space 2} .0137049{col 37}{space 1}   -0.27{col 46}{space 3}0.788{col 54}{space 4}-.0307309{col 67}{space 3} .0233569
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0031405{col 26}{space 2}  .007028{col 37}{space 1}    0.45{col 46}{space 3}0.656{col 54}{space 4} -.010728{col 67}{space 3}  .017009
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0196212{col 26}{space 2} .0018748{col 37}{space 1}   10.47{col 46}{space 3}0.000{col 54}{space 4} .0159216{col 67}{space 3} .0233208
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002253{col 26}{space 2} .0000205{col 37}{space 1}  -11.01{col 46}{space 3}0.000{col 54}{space 4}-.0002657{col 67}{space 3}-.0001849
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0068124{col 26}{space 2} .0119939{col 37}{space 1}   -0.57{col 46}{space 3}0.571{col 54}{space 4}-.0304799{col 67}{space 3} .0168552
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0373567{col 26}{space 2} .0163242{col 37}{space 1}    2.29{col 46}{space 3}0.023{col 54}{space 4}  .005144{col 67}{space 3} .0695693
{txt}{space 4}children {c |}{col 14}{res}{space 2}  .016833{col 26}{space 2} .0063352{col 37}{space 1}    2.66{col 46}{space 3}0.009{col 54}{space 4} .0043317{col 67}{space 3} .0293342
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0853437{col 26}{space 2}  .011256{col 37}{space 1}    7.58{col 46}{space 3}0.000{col 54}{space 4} .0631322{col 67}{space 3} .1075551
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4258381{col 26}{space 2} .0923334{col 37}{space 1}    4.61{col 46}{space 3}0.000{col 54}{space 4} .2436361{col 67}{space 3}   .60804
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .2803689{col 26}{space 2} .0976521{col 37}{space 1}    2.87{col 46}{space 3}0.005{col 54}{space 4} .0876716{col 67}{space 3} .4730663
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .1461722{col 26}{space 2}  .102322{col 37}{space 1}    1.43{col 46}{space 3}0.155{col 54}{space 4}-.0557403{col 67}{space 3} .3480847
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .3245853{col 26}{space 2} .1027338{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4} .1218601{col 67}{space 3} .5273104
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{txt}{space 9}43  {c |}{col 14}{res}{space 2} .2565622{col 26}{space 2} .0242732{col 37}{space 1}   10.57{col 46}{space 3}0.000{col 54}{space 4} .2086639{col 67}{space 3} .3044606
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1659338{col 26}{space 2} .0458508{col 37}{space 1}   -3.62{col 46}{space 3}0.000{col 54}{space 4}-.2564113{col 67}{space 3}-.0754562
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,301
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       749
{txt}{col 49}F({res}  77{txt},{res}    253{txt}){col 67}= {res}    134.18
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1508
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1023
{txt}{col 49}Root MSE{col 67}= {res}    0.4736

{txt}{ralign 78:(Std. Err. adjusted for {res:254} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0219391{col 26}{space 2} .0051211{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} .0118537{col 67}{space 3} .0320246
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{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}67  {c |}{col 14}{res}{space 2} .5284582{col 26}{space 2} .0860144{col 37}{space 1}    6.14{col 46}{space 3}0.000{col 54}{space 4} .3590627{col 67}{space 3} .6978538
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .6251964{col 26}{space 2} .0914618{col 37}{space 1}    6.84{col 46}{space 3}0.000{col 54}{space 4} .4450729{col 67}{space 3} .8053198
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .4813047{col 26}{space 2}  .084163{col 37}{space 1}    5.72{col 46}{space 3}0.000{col 54}{space 4} .3155553{col 67}{space 3}  .647054
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4718141{col 26}{space 2} .0871644{col 37}{space 1}    5.41{col 46}{space 3}0.000{col 54}{space 4} .3001538{col 67}{space 3} .6434743
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3332401{col 26}{space 2} .0771896{col 37}{space 1}   -4.32{col 46}{space 3}0.000{col 54}{space 4}-.4852562{col 67}{space 3}-.1812241
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg.tex"'})

{com}. 
. 
.  ///LONG TERM RESULTS
>  
.  preserve
{txt}
{com}.  ///long term: MERGE ON 2010 VOTING DATA
>  
. rename r r_first
{res}{txt}
{com}. rename k k_first
{res}{txt}
{com}. rename l l_first
{res}{txt}
{com}.  
.  
. merge 1:m LopNr using "D:\SCB_ConPol\Stata\VD\Valdelt_2010", keepusing (LopNr r k l)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,213,376
{txt}{col 9}from master{col 30}{res}          11,355{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,202,021{txt}  (_merge==2)

{col 5}matched{col 30}{res}          52,018{txt}  (_merge==3)
{col 5}{hline 41}

{com}. keep if _merge==3
{txt}(7,213,376 observations deleted)

{com}. 
. 
. *check for duplicates - OK
. *duplicates report LopNr 
. 
. ///create voting variable
> 
. ///voted or not
> gen voted_k2010 =0 if k==1
{txt}(36,940 missing values generated)

{com}. replace voted_k2010=1 if voted_k2010==.
{txt}(36,940 real changes made)

{com}. 
. label variable voted_k2010  "voted in local election 2010"
{txt}
{com}. 
. ///Baseline reg
> eststo clear
{txt}
{com}. 
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    49,990
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}     1,006
{txt}{col 49}F({res}  77{txt},{res}    284{txt}){col 67}= {res}     65.48
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0695
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0490
{txt}{col 49}Root MSE{col 67}= {res}    0.4414

{txt}{ralign 78:(Std. Err. adjusted for {res:285} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0022564{col 26}{space 2} .0035621{col 37}{space 1}   -0.63{col 46}{space 3}0.527{col 54}{space 4}-.0092678{col 67}{space 3} .0047551
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0268812{col 26}{space 2} .0054461{col 37}{space 1}   -4.94{col 46}{space 3}0.000{col 54}{space 4}-.0376011{col 67}{space 3}-.0161613
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0147823{col 26}{space 2} .0011478{col 37}{space 1}   12.88{col 46}{space 3}0.000{col 54}{space 4} .0125231{col 67}{space 3} .0170415
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002483{col 26}{space 2} .0000138{col 37}{space 1}  -18.03{col 46}{space 3}0.000{col 54}{space 4}-.0002754{col 67}{space 3}-.0002212
{txt}{space 5}married {c |}{col 14}{res}{space 2}  .014514{col 26}{space 2} .0052828{col 37}{space 1}    2.75{col 46}{space 3}0.006{col 54}{space 4} .0041156{col 67}{space 3} .0249124
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0127946{col 26}{space 2} .0071108{col 37}{space 1}    1.80{col 46}{space 3}0.073{col 54}{space 4} -.001202{col 67}{space 3} .0267912
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0017996{col 26}{space 2} .0023272{col 37}{space 1}    0.77{col 46}{space 3}0.440{col 54}{space 4}-.0027812{col 67}{space 3} .0063804
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0523057{col 26}{space 2}  .005624{col 37}{space 1}    9.30{col 46}{space 3}0.000{col 54}{space 4} .0412358{col 67}{space 3} .0633757
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .7019557{col 26}{space 2} .1432713{col 37}{space 1}    4.90{col 46}{space 3}0.000{col 54}{space 4} .4199473{col 67}{space 3} .9839641
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .3140178{col 26}{space 2} .1686381{col 37}{space 1}    1.86{col 46}{space 3}0.064{col 54}{space 4}-.0179214{col 67}{space 3}  .645957
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2353429{col 26}{space 2} .2903699{col 37}{space 1}    0.81{col 46}{space 3}0.418{col 54}{space 4}-.3362073{col 67}{space 3}  .806893
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0306853{col 26}{space 2} .2088924{col 37}{space 1}    0.15{col 46}{space 3}0.883{col 54}{space 4}-.3804885{col 67}{space 3} .4418591
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1783046{col 26}{space 2} .1475147{col 37}{space 1}    1.21{col 46}{space 3}0.228{col 54}{space 4}-.1120564{col 67}{space 3} .4686655
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1970195{col 26}{space 2} .1440194{col 37}{space 1}    1.37{col 46}{space 3}0.172{col 54}{space 4}-.0864614{col 67}{space 3} .4805005
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2689478{col 26}{space 2} .1491328{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4} -.024598{col 67}{space 3} .5624937
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2930764{col 26}{space 2} .1466465{col 37}{space 1}    2.00{col 46}{space 3}0.047{col 54}{space 4} .0044244{col 67}{space 3} .5817284
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2570867{col 26}{space 2} .1481665{col 37}{space 1}    1.74{col 46}{space 3}0.084{col 54}{space 4}-.0345572{col 67}{space 3} .5487307
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4718895{col 26}{space 2} .1481849{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4} .1802093{col 67}{space 3} .7635696
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{txt}{space 9}13  {c |}{col 14}{res}{space 2} .4674933{col 26}{space 2} .1508309{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4}  .170605{col 67}{space 3} .7643816
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{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5223862{col 26}{space 2} .1503883{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4} .2263691{col 67}{space 3} .8184032
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .4334037{col 26}{space 2} .1456543{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .1467047{col 67}{space 3} .7201027
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .4399531{col 26}{space 2} .1471793{col 37}{space 1}    2.99{col 46}{space 3}0.003{col 54}{space 4} .1502524{col 67}{space 3} .7296539
{txt}{space 9}18  {c |}{col 14}{res}{space 2}  .408594{col 26}{space 2} .1500216{col 37}{space 1}    2.72{col 46}{space 3}0.007{col 54}{space 4} .1132985{col 67}{space 3} .7038894
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .3885325{col 26}{space 2} .1484679{col 37}{space 1}    2.62{col 46}{space 3}0.009{col 54}{space 4} .0962954{col 67}{space 3} .6807696
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{txt}{space 9}29  {c |}{col 14}{res}{space 2} .4604918{col 26}{space 2}  .150755{col 37}{space 1}    3.05{col 46}{space 3}0.002{col 54}{space 4} .1637528{col 67}{space 3} .7572307
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .4453087{col 26}{space 2} .1455837{col 37}{space 1}    3.06{col 46}{space 3}0.002{col 54}{space 4} .1587488{col 67}{space 3} .7318687
{txt}{space 9}31  {c |}{col 14}{res}{space 2}  .382341{col 26}{space 2} .1418176{col 37}{space 1}    2.70{col 46}{space 3}0.007{col 54}{space 4} .1031939{col 67}{space 3}  .661488
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .3624255{col 26}{space 2} .1474153{col 37}{space 1}    2.46{col 46}{space 3}0.015{col 54}{space 4} .0722603{col 67}{space 3} .6525908
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .3831375{col 26}{space 2} .1485616{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4} .0907159{col 67}{space 3}  .675559
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{txt}{space 9}47  {c |}{col 14}{res}{space 2} .4274045{col 26}{space 2} .1510264{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .1301313{col 67}{space 3} .7246777
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .3920587{col 26}{space 2} .1509732{col 37}{space 1}    2.60{col 46}{space 3}0.010{col 54}{space 4} .0948904{col 67}{space 3} .6892271
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .4040441{col 26}{space 2} .1427328{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .1230956{col 67}{space 3} .6849926
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{txt}{space 9}68  {c |}{col 14}{res}{space 2} .4032261{col 26}{space 2} .1545116{col 37}{space 1}    2.61{col 46}{space 3}0.010{col 54}{space 4} .0990929{col 67}{space 3} .7073593
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{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4372776{col 26}{space 2} .1671182{col 37}{space 1}    2.62{col 46}{space 3}0.009{col 54}{space 4} .1083301{col 67}{space 3}  .766225
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .1108516{col 26}{space 2} .1442784{col 37}{space 1}    0.77{col 46}{space 3}0.443{col 54}{space 4}-.1731391{col 67}{space 3} .3948423
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    13,186
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       791
{txt}{col 49}F({res}  73{txt},{res}    277{txt}){col 67}= {res}      8.76
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1015
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0386
{txt}{col 49}Root MSE{col 67}= {res}    0.4348

{txt}{ralign 78:(Std. Err. adjusted for {res:278} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0037565{col 26}{space 2} .0093963{col 37}{space 1}    0.40{col 46}{space 3}0.690{col 54}{space 4}-.0147407{col 67}{space 3} .0222538
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0368784{col 26}{space 2} .0092387{col 37}{space 1}   -3.99{col 46}{space 3}0.000{col 54}{space 4}-.0550655{col 67}{space 3}-.0186914
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0174313{col 26}{space 2} .0025449{col 37}{space 1}    6.85{col 46}{space 3}0.000{col 54}{space 4} .0124214{col 67}{space 3} .0224411
{txt}{space 8}age2 {c |}{col 14}{res}{space 2} -.000279{col 26}{space 2}  .000034{col 37}{space 1}   -8.20{col 46}{space 3}0.000{col 54}{space 4} -.000346{col 67}{space 3} -.000212
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0128097{col 26}{space 2} .0120304{col 37}{space 1}    1.06{col 46}{space 3}0.288{col 54}{space 4}-.0108729{col 67}{space 3} .0364924
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0269893{col 26}{space 2} .0153679{col 37}{space 1}    1.76{col 46}{space 3}0.080{col 54}{space 4}-.0032634{col 67}{space 3}  .057242
{txt}{space 4}children {c |}{col 14}{res}{space 2}-.0044623{col 26}{space 2} .0047296{col 37}{space 1}   -0.94{col 46}{space 3}0.346{col 54}{space 4}-.0137729{col 67}{space 3} .0048482
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0277511{col 26}{space 2} .0103231{col 37}{space 1}    2.69{col 46}{space 3}0.008{col 54}{space 4} .0074294{col 67}{space 3} .0480728
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.7689626{col 26}{space 2} .1954683{col 37}{space 1}   -3.93{col 46}{space 3}0.000{col 54}{space 4}-1.153755{col 67}{space 3}-.3841705
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.6281897{col 26}{space 2} .3416718{col 37}{space 1}   -1.84{col 46}{space 3}0.067{col 54}{space 4}-1.300793{col 67}{space 3} .0444135
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2}-.4648024{col 26}{space 2} .1606815{col 37}{space 1}   -2.89{col 46}{space 3}0.004{col 54}{space 4}-.7811144{col 67}{space 3}-.1484904
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.4228108{col 26}{space 2} .1159064{col 37}{space 1}   -3.65{col 46}{space 3}0.000{col 54}{space 4}  -.65098{col 67}{space 3}-.1946416
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.5586276{col 26}{space 2} .1242521{col 37}{space 1}   -4.50{col 46}{space 3}0.000{col 54}{space 4} -.803226{col 67}{space 3}-.3140292
{txt}{space 9}11  {c |}{col 14}{res}{space 2} -.267202{col 26}{space 2} .0683901{col 37}{space 1}   -3.91{col 46}{space 3}0.000{col 54}{space 4}-.4018323{col 67}{space 3}-.1325717
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.3266423{col 26}{space 2} .0674246{col 37}{space 1}   -4.84{col 46}{space 3}0.000{col 54}{space 4}-.4593719{col 67}{space 3}-.1939126
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.3039509{col 26}{space 2} .0764167{col 37}{space 1}   -3.98{col 46}{space 3}0.000{col 54}{space 4}-.4543822{col 67}{space 3}-.1535196
{txt}{space 9}14  {c |}{col 14}{res}{space 2} -.362703{col 26}{space 2} .0692596{col 37}{space 1}   -5.24{col 46}{space 3}0.000{col 54}{space 4}-.4990451{col 67}{space 3} -.226361
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.2909896{col 26}{space 2} .0770985{col 37}{space 1}   -3.77{col 46}{space 3}0.000{col 54}{space 4}-.4427629{col 67}{space 3}-.1392163
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.3429563{col 26}{space 2} .0577494{col 37}{space 1}   -5.94{col 46}{space 3}0.000{col 54}{space 4}-.4566398{col 67}{space 3}-.2292729
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-.3390855{col 26}{space 2} .0422573{col 37}{space 1}   -8.02{col 46}{space 3}0.000{col 54}{space 4}-.4222718{col 67}{space 3}-.2558992
{txt}{space 9}18  {c |}{col 14}{res}{space 2}-.3946958{col 26}{space 2} .0426275{col 37}{space 1}   -9.26{col 46}{space 3}0.000{col 54}{space 4}-.4786108{col 67}{space 3}-.3107807
{txt}{space 9}19  {c |}{col 14}{res}{space 2}-.3938651{col 26}{space 2} .0648783{col 37}{space 1}   -6.07{col 46}{space 3}0.000{col 54}{space 4}-.5215822{col 67}{space 3} -.266148
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.4684374{col 26}{space 2} .1669384{col 37}{space 1}   -2.81{col 46}{space 3}0.005{col 54}{space 4}-.7970665{col 67}{space 3}-.1398084
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.4650273{col 26}{space 2} .1802342{col 37}{space 1}   -2.58{col 46}{space 3}0.010{col 54}{space 4}  -.81983{col 67}{space 3}-.1102246
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.2120546{col 26}{space 2} .0965157{col 37}{space 1}   -2.20{col 46}{space 3}0.029{col 54}{space 4} -.402052{col 67}{space 3}-.0220571
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.3296343{col 26}{space 2}  .090529{col 37}{space 1}   -3.64{col 46}{space 3}0.000{col 54}{space 4}-.5078465{col 67}{space 3}-.1514221
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.2321168{col 26}{space 2} .0650231{col 37}{space 1}   -3.57{col 46}{space 3}0.000{col 54}{space 4}-.3601189{col 67}{space 3}-.1041146
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-.3277902{col 26}{space 2} .1084068{col 37}{space 1}   -3.02{col 46}{space 3}0.003{col 54}{space 4} -.541196{col 67}{space 3}-.1143843
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.3692396{col 26}{space 2}  .053237{col 37}{space 1}   -6.94{col 46}{space 3}0.000{col 54}{space 4}-.4740401{col 67}{space 3} -.264439
{txt}{space 9}27  {c |}{col 14}{res}{space 2}-.2707876{col 26}{space 2} .0451261{col 37}{space 1}   -6.00{col 46}{space 3}0.000{col 54}{space 4}-.3596212{col 67}{space 3} -.181954
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.3350076{col 26}{space 2} .0507783{col 37}{space 1}   -6.60{col 46}{space 3}0.000{col 54}{space 4}-.4349679{col 67}{space 3}-.2350472
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.3215606{col 26}{space 2} .0471972{col 37}{space 1}   -6.81{col 46}{space 3}0.000{col 54}{space 4}-.4144714{col 67}{space 3}-.2286499
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-.3196432{col 26}{space 2} .0477301{col 37}{space 1}   -6.70{col 46}{space 3}0.000{col 54}{space 4}-.4136031{col 67}{space 3}-.2256833
{txt}{space 9}31  {c |}{col 14}{res}{space 2}-.3320301{col 26}{space 2} .0862835{col 37}{space 1}   -3.85{col 46}{space 3}0.000{col 54}{space 4}-.5018848{col 67}{space 3}-.1621754
{txt}{space 9}32  {c |}{col 14}{res}{space 2}-.4190264{col 26}{space 2} .0635176{col 37}{space 1}   -6.60{col 46}{space 3}0.000{col 54}{space 4} -.544065{col 67}{space 3}-.2939878
{txt}{space 9}33  {c |}{col 14}{res}{space 2}-.3985811{col 26}{space 2} .0412913{col 37}{space 1}   -9.65{col 46}{space 3}0.000{col 54}{space 4}-.4798658{col 67}{space 3}-.3172965
{txt}{space 9}34  {c |}{col 14}{res}{space 2}-.3610264{col 26}{space 2} .0482817{col 37}{space 1}   -7.48{col 46}{space 3}0.000{col 54}{space 4} -.456072{col 67}{space 3}-.2659807
{txt}{space 9}35  {c |}{col 14}{res}{space 2}-.4408467{col 26}{space 2} .0535865{col 37}{space 1}   -8.23{col 46}{space 3}0.000{col 54}{space 4}-.5463352{col 67}{space 3}-.3353582
{txt}{space 9}36  {c |}{col 14}{res}{space 2}-.4879734{col 26}{space 2} .1818488{col 37}{space 1}   -2.68{col 46}{space 3}0.008{col 54}{space 4}-.8459547{col 67}{space 3}-.1299922
{txt}{space 9}37  {c |}{col 14}{res}{space 2}-.3743032{col 26}{space 2} .1722817{col 37}{space 1}   -2.17{col 46}{space 3}0.031{col 54}{space 4}-.7134509{col 67}{space 3}-.0351555
{txt}{space 9}38  {c |}{col 14}{res}{space 2} -.275342{col 26}{space 2} .0666544{col 37}{space 1}   -4.13{col 46}{space 3}0.000{col 54}{space 4}-.4065555{col 67}{space 3}-.1441284
{txt}{space 9}39  {c |}{col 14}{res}{space 2}-.3569866{col 26}{space 2}  .119701{col 37}{space 1}   -2.98{col 46}{space 3}0.003{col 54}{space 4}-.5926259{col 67}{space 3}-.1213473
{txt}{space 9}40  {c |}{col 14}{res}{space 2}-.2322679{col 26}{space 2}  .078618{col 37}{space 1}   -2.95{col 46}{space 3}0.003{col 54}{space 4}-.3870326{col 67}{space 3}-.0775033
{txt}{space 9}41  {c |}{col 14}{res}{space 2} -.318635{col 26}{space 2}  .041103{col 37}{space 1}   -7.75{col 46}{space 3}0.000{col 54}{space 4}-.3995488{col 67}{space 3}-.2377211
{txt}{space 9}42  {c |}{col 14}{res}{space 2}-.3314238{col 26}{space 2} .0399074{col 37}{space 1}   -8.30{col 46}{space 3}0.000{col 54}{space 4}-.4099841{col 67}{space 3}-.2528634
{txt}{space 9}43  {c |}{col 14}{res}{space 2}-.3701038{col 26}{space 2} .0427045{col 37}{space 1}   -8.67{col 46}{space 3}0.000{col 54}{space 4}-.4541704{col 67}{space 3}-.2860372
{txt}{space 9}44  {c |}{col 14}{res}{space 2}-.3624466{col 26}{space 2}  .044931{col 37}{space 1}   -8.07{col 46}{space 3}0.000{col 54}{space 4}-.4508962{col 67}{space 3}-.2739969
{txt}{space 9}45  {c |}{col 14}{res}{space 2}-.3503924{col 26}{space 2} .0442607{col 37}{space 1}   -7.92{col 46}{space 3}0.000{col 54}{space 4}-.4375225{col 67}{space 3}-.2632622
{txt}{space 9}46  {c |}{col 14}{res}{space 2}-.3924845{col 26}{space 2} .0655458{col 37}{space 1}   -5.99{col 46}{space 3}0.000{col 54}{space 4}-.5215156{col 67}{space 3}-.2634535
{txt}{space 9}47  {c |}{col 14}{res}{space 2}-.3307527{col 26}{space 2} .0469638{col 37}{space 1}   -7.04{col 46}{space 3}0.000{col 54}{space 4} -.423204{col 67}{space 3}-.2383015
{txt}{space 9}48  {c |}{col 14}{res}{space 2}-.3848239{col 26}{space 2} .0475976{col 37}{space 1}   -8.08{col 46}{space 3}0.000{col 54}{space 4}-.4785228{col 67}{space 3} -.291125
{txt}{space 9}49  {c |}{col 14}{res}{space 2}-.3265368{col 26}{space 2}  .045208{col 37}{space 1}   -7.22{col 46}{space 3}0.000{col 54}{space 4}-.4155316{col 67}{space 3}-.2375419
{txt}{space 9}50  {c |}{col 14}{res}{space 2}-.3363867{col 26}{space 2} .0462202{col 37}{space 1}   -7.28{col 46}{space 3}0.000{col 54}{space 4}-.4273741{col 67}{space 3}-.2453993
{txt}{space 9}51  {c |}{col 14}{res}{space 2}-.3111591{col 26}{space 2} .1096205{col 37}{space 1}   -2.84{col 46}{space 3}0.005{col 54}{space 4}-.5269542{col 67}{space 3}-.0953641
{txt}{space 9}52  {c |}{col 14}{res}{space 2}-.2686044{col 26}{space 2} .1017844{col 37}{space 1}   -2.64{col 46}{space 3}0.009{col 54}{space 4}-.4689735{col 67}{space 3}-.0682352
{txt}{space 9}53  {c |}{col 14}{res}{space 2}-.3741111{col 26}{space 2} .0830665{col 37}{space 1}   -4.50{col 46}{space 3}0.000{col 54}{space 4} -.537633{col 67}{space 3}-.2105892
{txt}{space 9}54  {c |}{col 14}{res}{space 2}-.2703647{col 26}{space 2} .0718812{col 37}{space 1}   -3.76{col 46}{space 3}0.000{col 54}{space 4}-.4118675{col 67}{space 3}-.1288619
{txt}{space 9}55  {c |}{col 14}{res}{space 2}-.2442436{col 26}{space 2} .0762936{col 37}{space 1}   -3.20{col 46}{space 3}0.002{col 54}{space 4}-.3944326{col 67}{space 3}-.0940546
{txt}{space 9}56  {c |}{col 14}{res}{space 2}-.7206857{col 26}{space 2} .2034238{col 37}{space 1}   -3.54{col 46}{space 3}0.000{col 54}{space 4}-1.121139{col 67}{space 3}-.3202328
{txt}{space 9}57  {c |}{col 14}{res}{space 2}-.2509626{col 26}{space 2} .1230536{col 37}{space 1}   -2.04{col 46}{space 3}0.042{col 54}{space 4}-.4932017{col 67}{space 3}-.0087235
{txt}{space 9}58  {c |}{col 14}{res}{space 2}-.5886767{col 26}{space 2} .0865011{col 37}{space 1}   -6.81{col 46}{space 3}0.000{col 54}{space 4}-.7589597{col 67}{space 3}-.4183937
{txt}{space 9}59  {c |}{col 14}{res}{space 2} -.647352{col 26}{space 2} .0790679{col 37}{space 1}   -8.19{col 46}{space 3}0.000{col 54}{space 4}-.8030023{col 67}{space 3}-.4917017
{txt}{space 9}60  {c |}{col 14}{res}{space 2}-.7085319{col 26}{space 2} .1808901{col 37}{space 1}   -3.92{col 46}{space 3}0.000{col 54}{space 4}-1.064626{col 67}{space 3} -.352438
{txt}{space 9}61  {c |}{col 14}{res}{space 2}-.6212625{col 26}{space 2} .0966912{col 37}{space 1}   -6.43{col 46}{space 3}0.000{col 54}{space 4}-.8116054{col 67}{space 3}-.4309196
{txt}{space 9}62  {c |}{col 14}{res}{space 2}-.6600336{col 26}{space 2}  .097335{col 37}{space 1}   -6.78{col 46}{space 3}0.000{col 54}{space 4} -.851644{col 67}{space 3}-.4684233
{txt}{space 9}63  {c |}{col 14}{res}{space 2}-.5018581{col 26}{space 2} .0968467{col 37}{space 1}   -5.18{col 46}{space 3}0.000{col 54}{space 4}-.6925071{col 67}{space 3}-.3112091
{txt}{space 9}64  {c |}{col 14}{res}{space 2}-.5552865{col 26}{space 2} .0612096{col 37}{space 1}   -9.07{col 46}{space 3}0.000{col 54}{space 4}-.6757817{col 67}{space 3}-.4347914
{txt}{space 9}65  {c |}{col 14}{res}{space 2} -.587823{col 26}{space 2} .0603538{col 37}{space 1}   -9.74{col 46}{space 3}0.000{col 54}{space 4}-.7066334{col 67}{space 3}-.4690126
{txt}{space 9}66  {c |}{col 14}{res}{space 2}  -.45644{col 26}{space 2} .1301781{col 37}{space 1}   -3.51{col 46}{space 3}0.001{col 54}{space 4} -.712704{col 67}{space 3}-.2001761
{txt}{space 9}67  {c |}{col 14}{res}{space 2}-.5486666{col 26}{space 2} .1473465{col 37}{space 1}   -3.72{col 46}{space 3}0.000{col 54}{space 4}-.8387277{col 67}{space 3}-.2586055
{txt}{space 9}68  {c |}{col 14}{res}{space 2}-.2945801{col 26}{space 2} .0899289{col 37}{space 1}   -3.28{col 46}{space 3}0.001{col 54}{space 4} -.471611{col 67}{space 3}-.1175492
{txt}{space 9}69  {c |}{col 14}{res}{space 2}-.2389152{col 26}{space 2} .0849906{col 37}{space 1}   -2.81{col 46}{space 3}0.005{col 54}{space 4}-.4062246{col 67}{space 3}-.0716057
{txt}{space 9}70  {c |}{col 14}{res}{space 2}-.4788104{col 26}{space 2} .1017553{col 37}{space 1}   -4.71{col 46}{space 3}0.000{col 54}{space 4}-.6791223{col 67}{space 3}-.2784985
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .8410114{col 26}{space 2} .0612165{col 37}{space 1}   13.74{col 46}{space 3}0.000{col 54}{space 4} .7205028{col 67}{space 3}   .96152
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    12,861
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       729
{txt}{col 49}{help j_robustsingular##|_new:F(  72,    275)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1044
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0448
{txt}{col 49}Root MSE{col 67}= {res}    0.4359

{txt}{ralign 78:(Std. Err. adjusted for {res:276} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0115697{col 26}{space 2} .0115888{col 37}{space 1}    1.00{col 46}{space 3}0.319{col 54}{space 4}-.0112443{col 67}{space 3} .0343837
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0337832{col 26}{space 2} .0085635{col 37}{space 1}   -3.95{col 46}{space 3}0.000{col 54}{space 4}-.0506414{col 67}{space 3}-.0169249
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0144602{col 26}{space 2} .0022427{col 37}{space 1}    6.45{col 46}{space 3}0.000{col 54}{space 4} .0100451{col 67}{space 3} .0188753
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}  -.00024{col 26}{space 2} .0000297{col 37}{space 1}   -8.08{col 46}{space 3}0.000{col 54}{space 4}-.0002984{col 67}{space 3}-.0001815
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0162076{col 26}{space 2} .0129181{col 37}{space 1}    1.25{col 46}{space 3}0.211{col 54}{space 4}-.0092234{col 67}{space 3} .0416385
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0082013{col 26}{space 2}  .014487{col 37}{space 1}    0.57{col 46}{space 3}0.572{col 54}{space 4}-.0203182{col 67}{space 3} .0367207
{txt}{space 4}children {c |}{col 14}{res}{space 2}-.0027108{col 26}{space 2} .0049501{col 37}{space 1}   -0.55{col 46}{space 3}0.584{col 54}{space 4}-.0124558{col 67}{space 3} .0070341
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0465203{col 26}{space 2} .0102626{col 37}{space 1}    4.53{col 46}{space 3}0.000{col 54}{space 4} .0263171{col 67}{space 3} .0667235
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2} .7254949{col 26}{space 2} .0424666{col 37}{space 1}   17.08{col 46}{space 3}0.000{col 54}{space 4} .6418939{col 67}{space 3} .8090958
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4677898{col 26}{space 2}  .356411{col 37}{space 1}    1.31{col 46}{space 3}0.190{col 54}{space 4}-.2338509{col 67}{space 3}  1.16943
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1526172{col 26}{space 2} .0427295{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .0684987{col 67}{space 3} .2367357
{txt}{space 10}6  {c |}{col 14}{res}{space 2} 1.112583{col 26}{space 2} .0881777{col 37}{space 1}   12.62{col 46}{space 3}0.000{col 54}{space 4} .9389941{col 67}{space 3} 1.286172
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .8037981{col 26}{space 2} .1250224{col 37}{space 1}    6.43{col 46}{space 3}0.000{col 54}{space 4} .5576755{col 67}{space 3} 1.049921
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .7602226{col 26}{space 2} .1540073{col 37}{space 1}    4.94{col 46}{space 3}0.000{col 54}{space 4} .4570395{col 67}{space 3} 1.063406
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0463313{col 26}{space 2} .1143437{col 37}{space 1}    0.41{col 46}{space 3}0.686{col 54}{space 4}-.1787689{col 67}{space 3} .2714316
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .7417848{col 26}{space 2} .1004321{col 37}{space 1}    7.39{col 46}{space 3}0.000{col 54}{space 4} .5440715{col 67}{space 3} .9394982
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{txt}{space 9}69  {c |}{col 14}{res}{space 2}  .808463{col 26}{space 2} .0773092{col 37}{space 1}   10.46{col 46}{space 3}0.000{col 54}{space 4} .6562701{col 67}{space 3}  .960656
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .6925067{col 26}{space 2} .0784718{col 37}{space 1}    8.82{col 46}{space 3}0.000{col 54}{space 4}  .538025{col 67}{space 3} .8469885
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1756639{col 26}{space 2} .0509059{col 37}{space 1}   -3.45{col 46}{space 3}0.001{col 54}{space 4}-.2758786{col 67}{space 3}-.0754492
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    12,387
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       401
{txt}{col 49}F({res}  72{txt},{res}    173{txt}){col 67}= {res}     60.04
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0921
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0561
{txt}{col 49}Root MSE{col 67}= {res}    0.4433

{txt}{ralign 78:(Std. Err. adjusted for {res:174} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}  .005452{col 26}{space 2} .0122466{col 37}{space 1}    0.45{col 46}{space 3}0.657{col 54}{space 4}  -.01872{col 67}{space 3}  .029624
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0345552{col 26}{space 2} .0085692{col 37}{space 1}   -4.03{col 46}{space 3}0.000{col 54}{space 4}-.0514689{col 67}{space 3}-.0176415
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0187928{col 26}{space 2} .0025336{col 37}{space 1}    7.42{col 46}{space 3}0.000{col 54}{space 4}  .013792{col 67}{space 3} .0237935
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002944{col 26}{space 2} .0000312{col 37}{space 1}   -9.45{col 46}{space 3}0.000{col 54}{space 4}-.0003559{col 67}{space 3}-.0002329
{txt}{space 5}married {c |}{col 14}{res}{space 2}  .031004{col 26}{space 2} .0106333{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .0100163{col 67}{space 3} .0519917
{txt}{space 6}parent {c |}{col 14}{res}{space 2}-.0007817{col 26}{space 2} .0152677{col 37}{space 1}   -0.05{col 46}{space 3}0.959{col 54}{space 4}-.0309166{col 67}{space 3} .0293532
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0018235{col 26}{space 2}  .005882{col 37}{space 1}    0.31{col 46}{space 3}0.757{col 54}{space 4}-.0097863{col 67}{space 3} .0134332
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0537426{col 26}{space 2} .0104924{col 37}{space 1}    5.12{col 46}{space 3}0.000{col 54}{space 4} .0330329{col 67}{space 3} .0744522
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}11  {c |}{col 14}{res}{space 2} .5366005{col 26}{space 2} .2177528{col 37}{space 1}    2.46{col 46}{space 3}0.015{col 54}{space 4} .1068063{col 67}{space 3} .9663948
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .456504{col 26}{space 2}  .269194{col 37}{space 1}    1.70{col 46}{space 3}0.092{col 54}{space 4}-.0748235{col 67}{space 3} .9878314
{txt}{space 9}13  {c |}{col 14}{res}{space 2}  .395457{col 26}{space 2}  .226761{col 37}{space 1}    1.74{col 46}{space 3}0.083{col 54}{space 4}-.0521173{col 67}{space 3} .8430313
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .4981078{col 26}{space 2} .2573164{col 37}{space 1}    1.94{col 46}{space 3}0.055{col 54}{space 4}-.0097759{col 67}{space 3} 1.005991
{txt}{space 9}15  {c |}{col 14}{res}{space 2}  .695899{col 26}{space 2} .1994842{col 37}{space 1}    3.49{col 46}{space 3}0.001{col 54}{space 4} .3021628{col 67}{space 3} 1.089635
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .3734659{col 26}{space 2} .2021118{col 37}{space 1}    1.85{col 46}{space 3}0.066{col 54}{space 4}-.0254565{col 67}{space 3} .7723884
{txt}{space 9}17  {c |}{col 14}{res}{space 2}  .348818{col 26}{space 2} .1981905{col 37}{space 1}    1.76{col 46}{space 3}0.080{col 54}{space 4}-.0423648{col 67}{space 3} .7400009
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .4011994{col 26}{space 2}   .19718{col 37}{space 1}    2.03{col 46}{space 3}0.043{col 54}{space 4} .0120113{col 67}{space 3} .7903875
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2998791{col 26}{space 2} .2016391{col 37}{space 1}    1.49{col 46}{space 3}0.139{col 54}{space 4}-.0981103{col 67}{space 3} .6978685
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{txt}{space 9}24  {c |}{col 14}{res}{space 2}  .488035{col 26}{space 2} .2301327{col 37}{space 1}    2.12{col 46}{space 3}0.035{col 54}{space 4} .0338056{col 67}{space 3} .9422644
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{txt}{space 9}27  {c |}{col 14}{res}{space 2} .4578574{col 26}{space 2} .2045257{col 37}{space 1}    2.24{col 46}{space 3}0.026{col 54}{space 4} .0541704{col 67}{space 3} .8615444
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .3987501{col 26}{space 2} .2036936{col 37}{space 1}    1.96{col 46}{space 3}0.052{col 54}{space 4}-.0032946{col 67}{space 3} .8007947
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .4646032{col 26}{space 2}    .2043{col 37}{space 1}    2.27{col 46}{space 3}0.024{col 54}{space 4} .0613617{col 67}{space 3} .8678447
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .4048892{col 26}{space 2} .2045512{col 37}{space 1}    1.98{col 46}{space 3}0.049{col 54}{space 4} .0011519{col 67}{space 3} .8086265
{txt}{space 9}31  {c |}{col 14}{res}{space 2}  .434153{col 26}{space 2} .2042378{col 37}{space 1}    2.13{col 46}{space 3}0.035{col 54}{space 4} .0310343{col 67}{space 3} .8372717
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0510323{col 26}{space 2} .2182453{col 37}{space 1}    0.23{col 46}{space 3}0.815{col 54}{space 4}-.3797339{col 67}{space 3} .4817985
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    11,556
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       681
{txt}{col 49}F({res}  77{txt},{res}    242{txt}){col 67}= {res}    131.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1390
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0786
{txt}{col 49}Root MSE{col 67}= {res}    0.4462

{txt}{ralign 78:(Std. Err. adjusted for {res:243} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0018293{col 26}{space 2}  .004602{col 37}{space 1}   -0.40{col 46}{space 3}0.691{col 54}{space 4}-.0108945{col 67}{space 3} .0072359
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0015537{col 26}{space 2}  .013339{col 37}{space 1}   -0.12{col 46}{space 3}0.907{col 54}{space 4}-.0278291{col 67}{space 3} .0247217
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0068353{col 26}{space 2} .0016852{col 37}{space 1}    4.06{col 46}{space 3}0.000{col 54}{space 4} .0035159{col 67}{space 3} .0101548
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001574{col 26}{space 2}  .000022{col 37}{space 1}   -7.14{col 46}{space 3}0.000{col 54}{space 4}-.0002008{col 67}{space 3}-.0001139
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0073548{col 26}{space 2} .0111282{col 37}{space 1}    0.66{col 46}{space 3}0.509{col 54}{space 4}-.0145657{col 67}{space 3} .0292754
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{txt}{space 4}children {c |}{col 14}{res}{space 2} .0043902{col 26}{space 2} .0060536{col 37}{space 1}    0.73{col 46}{space 3}0.469{col 54}{space 4}-.0075344{col 67}{space 3} .0163147
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0733169{col 26}{space 2} .0093869{col 37}{space 1}    7.81{col 46}{space 3}0.000{col 54}{space 4} .0548265{col 67}{space 3} .0918073
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .4481172{col 26}{space 2} .0970215{col 37}{space 1}    4.62{col 46}{space 3}0.000{col 54}{space 4} .2570029{col 67}{space 3} .6392316
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reglong.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reglong.tex"'})

{com}. 
. 
. restore
{txt}
{com}. 
. ///////////////////////////////////////////
> /// AUXILIARY ANALYSIS  ///////////////////
> //////////////////////////////////////////
> 
. ///ALTERNATIVE MEASURE - TABLE A5
> 
. *Like the main table, TABLE A5 is also constructed by combining the long term 
. *and the short term tables directly in latex
. 
. ///using coethnicsvoted/coethnicstrighttovote
> eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    61,155
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}     1,063
{txt}{col 49}F({res}  77{txt},{res}    285{txt}){col 67}= {res}    148.66
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1090
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0921
{txt}{col 49}Root MSE{col 67}= {res}    0.4762

{txt}{ralign 78:(Std. Err. adjusted for {res:286} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0075737{col 26}{space 2} .0068444{col 37}{space 1}    1.11{col 46}{space 3}0.269{col 54}{space 4}-.0058982{col 67}{space 3} .0210456
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0185757{col 26}{space 2} .0046634{col 37}{space 1}    3.98{col 46}{space 3}0.000{col 54}{space 4} .0093967{col 67}{space 3} .0277548
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0195798{col 26}{space 2} .0012746{col 37}{space 1}   15.36{col 46}{space 3}0.000{col 54}{space 4}  .017071{col 67}{space 3} .0220886
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002302{col 26}{space 2} .0000148{col 37}{space 1}  -15.55{col 46}{space 3}0.000{col 54}{space 4}-.0002594{col 67}{space 3}-.0002011
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0122084{col 26}{space 2} .0051827{col 37}{space 1}    2.36{col 46}{space 3}0.019{col 54}{space 4} .0020072{col 67}{space 3} .0224096
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{txt}{space 4}children {c |}{col 14}{res}{space 2} .0153576{col 26}{space 2}  .003558{col 37}{space 1}    4.32{col 46}{space 3}0.000{col 54}{space 4} .0083544{col 67}{space 3} .0223609
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0788355{col 26}{space 2} .0056577{col 37}{space 1}   13.93{col 46}{space 3}0.000{col 54}{space 4} .0676993{col 67}{space 3} .0899717
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}63  {c |}{col 14}{res}{space 2} .2349685{col 26}{space 2} .1440818{col 37}{space 1}    1.63{col 46}{space 3}0.104{col 54}{space 4}-.0486311{col 67}{space 3}  .518568
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .1986347{col 26}{space 2} .1331169{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4}-.0633823{col 67}{space 3} .4606516
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .1182608{col 26}{space 2} .1204827{col 37}{space 1}    0.98{col 46}{space 3}0.327{col 54}{space 4}-.1188879{col 67}{space 3} .3554096
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .4784495{col 26}{space 2} .1451688{col 37}{space 1}    3.30{col 46}{space 3}0.001{col 54}{space 4} .1927104{col 67}{space 3} .7641886
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .3448382{col 26}{space 2} .1355398{col 37}{space 1}    2.54{col 46}{space 3}0.011{col 54}{space 4} .0780521{col 67}{space 3} .6116243
{txt}{space 9}68  {c |}{col 14}{res}{space 2}  .434078{col 26}{space 2} .1389109{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .1606565{col 67}{space 3} .7074995
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .3489918{col 26}{space 2} .1406742{col 37}{space 1}    2.48{col 46}{space 3}0.014{col 54}{space 4} .0720995{col 67}{space 3}  .625884
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .3104538{col 26}{space 2} .1440791{col 37}{space 1}    2.15{col 46}{space 3}0.032{col 54}{space 4} .0268596{col 67}{space 3}  .594048
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2393594{col 26}{space 2} .1364085{col 37}{space 1}   -1.75{col 46}{space 3}0.080{col 54}{space 4}-.5078554{col 67}{space 3} .0291366
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_alt_cum<0.25 & voted_coethnics_alt_cum!=., absorb(Forsamling) vce(cluster Kommun)
{txt}note: 4.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,341
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       896
{txt}{col 49}{help j_robustsingular##|_new:F(  73,    279)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1577
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1009
{txt}{col 49}Root MSE{col 67}= {res}    0.4717

{txt}{ralign 78:(Std. Err. adjusted for {res:280} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0004436{col 26}{space 2} .0095578{col 37}{space 1}   -0.05{col 46}{space 3}0.963{col 54}{space 4}-.0192581{col 67}{space 3} .0183709
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0308325{col 26}{space 2} .0088297{col 37}{space 1}    3.49{col 46}{space 3}0.001{col 54}{space 4} .0134511{col 67}{space 3} .0482138
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0251415{col 26}{space 2} .0017875{col 37}{space 1}   14.07{col 46}{space 3}0.000{col 54}{space 4} .0216228{col 67}{space 3} .0286601
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0003038{col 26}{space 2} .0000218{col 37}{space 1}  -13.94{col 46}{space 3}0.000{col 54}{space 4}-.0003468{col 67}{space 3}-.0002609
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0284332{col 26}{space 2} .0123597{col 37}{space 1}    2.30{col 46}{space 3}0.022{col 54}{space 4} .0041032{col 67}{space 3} .0527633
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0430112{col 26}{space 2} .0164242{col 37}{space 1}    2.62{col 46}{space 3}0.009{col 54}{space 4} .0106801{col 67}{space 3} .0753423
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0134593{col 26}{space 2} .0052797{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} .0030663{col 67}{space 3} .0238524
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0538992{col 26}{space 2} .0104375{col 37}{space 1}    5.16{col 46}{space 3}0.000{col 54}{space 4} .0333529{col 67}{space 3} .0744454
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.3545957{col 26}{space 2} .3226389{col 37}{space 1}   -1.10{col 46}{space 3}0.273{col 54}{space 4}-.9897114{col 67}{space 3} .2805201
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}5  {c |}{col 14}{res}{space 2} .6499403{col 26}{space 2} .0723756{col 37}{space 1}    8.98{col 46}{space 3}0.000{col 54}{space 4} .5074688{col 67}{space 3} .7924119
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.6156303{col 26}{space 2} .1465557{col 37}{space 1}   -4.20{col 46}{space 3}0.000{col 54}{space 4}-.9041256{col 67}{space 3}-.3271351
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0100421{col 26}{space 2} .2701116{col 37}{space 1}   -0.04{col 46}{space 3}0.970{col 54}{space 4}-.5417576{col 67}{space 3} .5216734
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.4363599{col 26}{space 2} .0975948{col 37}{space 1}   -4.47{col 46}{space 3}0.000{col 54}{space 4}-.6284755{col 67}{space 3}-.2442444
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.2890186{col 26}{space 2} .1228416{col 37}{space 1}   -2.35{col 46}{space 3}0.019{col 54}{space 4}-.5308327{col 67}{space 3}-.0472045
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.2835087{col 26}{space 2} .1153652{col 37}{space 1}   -2.46{col 46}{space 3}0.015{col 54}{space 4}-.5106055{col 67}{space 3}-.0564119
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1425482{col 26}{space 2} .0898974{col 37}{space 1}    1.59{col 46}{space 3}0.114{col 54}{space 4}-.0344151{col 67}{space 3} .3195115
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.0054892{col 26}{space 2} .0860933{col 37}{space 1}   -0.06{col 46}{space 3}0.949{col 54}{space 4}-.1749641{col 67}{space 3} .1639856
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.0037348{col 26}{space 2} .0883742{col 37}{space 1}   -0.04{col 46}{space 3}0.966{col 54}{space 4}-.1776998{col 67}{space 3} .1702301
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{txt}{space 9}15  {c |}{col 14}{res}{space 2} .0259639{col 26}{space 2} .0809887{col 37}{space 1}    0.32{col 46}{space 3}0.749{col 54}{space 4}-.1334627{col 67}{space 3} .1853905
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .1325858{col 26}{space 2}  .054828{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .0246566{col 67}{space 3} .2405149
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .0778487{col 26}{space 2} .0554843{col 37}{space 1}    1.40{col 46}{space 3}0.162{col 54}{space 4}-.0313724{col 67}{space 3} .1870698
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .0241248{col 26}{space 2} .0507339{col 37}{space 1}    0.48{col 46}{space 3}0.635{col 54}{space 4}-.0757449{col 67}{space 3} .1239946
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .0127252{col 26}{space 2} .0703345{col 37}{space 1}    0.18{col 46}{space 3}0.857{col 54}{space 4}-.1257285{col 67}{space 3} .1511788
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.2034075{col 26}{space 2} .1123774{col 37}{space 1}   -1.81{col 46}{space 3}0.071{col 54}{space 4}-.4246227{col 67}{space 3} .0178077
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.1377433{col 26}{space 2} .1501657{col 37}{space 1}   -0.92{col 46}{space 3}0.360{col 54}{space 4}-.4333448{col 67}{space 3} .1578583
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.0514109{col 26}{space 2} .1643759{col 37}{space 1}   -0.31{col 46}{space 3}0.755{col 54}{space 4}-.3749854{col 67}{space 3} .2721636
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .0139153{col 26}{space 2} .0972408{col 37}{space 1}    0.14{col 46}{space 3}0.886{col 54}{space 4}-.1775034{col 67}{space 3} .2053341
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.1755539{col 26}{space 2}  .104962{col 37}{space 1}   -1.67{col 46}{space 3}0.096{col 54}{space 4}-.3821719{col 67}{space 3} .0310641
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-.0844772{col 26}{space 2}  .121353{col 37}{space 1}   -0.70{col 46}{space 3}0.487{col 54}{space 4}-.3233609{col 67}{space 3} .1544065
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .0461121{col 26}{space 2} .0543736{col 37}{space 1}    0.85{col 46}{space 3}0.397{col 54}{space 4}-.0609225{col 67}{space 3} .1531468
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .0343386{col 26}{space 2} .0522337{col 37}{space 1}    0.66{col 46}{space 3}0.511{col 54}{space 4}-.0684837{col 67}{space 3} .1371608
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.1076338{col 26}{space 2} .0474106{col 37}{space 1}   -2.27{col 46}{space 3}0.024{col 54}{space 4}-.2009618{col 67}{space 3}-.0143058
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.1240979{col 26}{space 2} .0482902{col 37}{space 1}   -2.57{col 46}{space 3}0.011{col 54}{space 4}-.2191573{col 67}{space 3}-.0290385
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-.2028169{col 26}{space 2} .0468169{col 37}{space 1}   -4.33{col 46}{space 3}0.000{col 54}{space 4}-.2949761{col 67}{space 3}-.1106578
{txt}{space 9}31  {c |}{col 14}{res}{space 2}  .031243{col 26}{space 2} .0778448{col 37}{space 1}    0.40{col 46}{space 3}0.688{col 54}{space 4}-.1219946{col 67}{space 3} .1844807
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .0149325{col 26}{space 2} .0631068{col 37}{space 1}    0.24{col 46}{space 3}0.813{col 54}{space 4}-.1092936{col 67}{space 3} .1391585
{txt}{space 9}33  {c |}{col 14}{res}{space 2}-.0269448{col 26}{space 2} .0521846{col 37}{space 1}   -0.52{col 46}{space 3}0.606{col 54}{space 4}-.1296703{col 67}{space 3} .0757808
{txt}{space 9}34  {c |}{col 14}{res}{space 2}-.1759462{col 26}{space 2} .0510907{col 37}{space 1}   -3.44{col 46}{space 3}0.001{col 54}{space 4}-.2765185{col 67}{space 3} -.075374
{txt}{space 9}35  {c |}{col 14}{res}{space 2}-.1911935{col 26}{space 2} .0521892{col 37}{space 1}   -3.66{col 46}{space 3}0.000{col 54}{space 4} -.293928{col 67}{space 3}-.0884589
{txt}{space 9}36  {c |}{col 14}{res}{space 2}-.1063659{col 26}{space 2} .1482179{col 37}{space 1}   -0.72{col 46}{space 3}0.474{col 54}{space 4}-.3981332{col 67}{space 3} .1854014
{txt}{space 9}37  {c |}{col 14}{res}{space 2}  .029356{col 26}{space 2} .1268102{col 37}{space 1}    0.23{col 46}{space 3}0.817{col 54}{space 4}-.2202702{col 67}{space 3} .2789823
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .0308634{col 26}{space 2}   .08255{col 37}{space 1}    0.37{col 46}{space 3}0.709{col 54}{space 4}-.1316365{col 67}{space 3} .1933633
{txt}{space 9}39  {c |}{col 14}{res}{space 2}-.1112928{col 26}{space 2} .1557683{col 37}{space 1}   -0.71{col 46}{space 3}0.476{col 54}{space 4}-.4179232{col 67}{space 3} .1953375
{txt}{space 9}40  {c |}{col 14}{res}{space 2}  -.01489{col 26}{space 2} .0798479{col 37}{space 1}   -0.19{col 46}{space 3}0.852{col 54}{space 4}-.1720708{col 67}{space 3} .1422908
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .0235054{col 26}{space 2} .0454543{col 37}{space 1}    0.52{col 46}{space 3}0.605{col 54}{space 4}-.0659715{col 67}{space 3} .1129823
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .0593101{col 26}{space 2}   .04395{col 37}{space 1}    1.35{col 46}{space 3}0.178{col 54}{space 4}-.0272056{col 67}{space 3} .1458258
{txt}{space 9}43  {c |}{col 14}{res}{space 2}-.0458633{col 26}{space 2} .0462895{col 37}{space 1}   -0.99{col 46}{space 3}0.323{col 54}{space 4}-.1369844{col 67}{space 3} .0452577
{txt}{space 9}44  {c |}{col 14}{res}{space 2} -.072554{col 26}{space 2} .0475929{col 37}{space 1}   -1.52{col 46}{space 3}0.129{col 54}{space 4}-.1662408{col 67}{space 3} .0211328
{txt}{space 9}45  {c |}{col 14}{res}{space 2}-.0556182{col 26}{space 2} .0474237{col 37}{space 1}   -1.17{col 46}{space 3}0.242{col 54}{space 4}-.1489719{col 67}{space 3} .0377355
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .0033898{col 26}{space 2}  .064597{col 37}{space 1}    0.05{col 46}{space 3}0.958{col 54}{space 4}-.1237696{col 67}{space 3} .1305492
{txt}{space 9}47  {c |}{col 14}{res}{space 2}-.0243352{col 26}{space 2} .0498479{col 37}{space 1}   -0.49{col 46}{space 3}0.626{col 54}{space 4}-.1224609{col 67}{space 3} .0737906
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .0191432{col 26}{space 2}  .050729{col 37}{space 1}    0.38{col 46}{space 3}0.706{col 54}{space 4}-.0807169{col 67}{space 3} .1190034
{txt}{space 9}49  {c |}{col 14}{res}{space 2}-.0453771{col 26}{space 2} .0482308{col 37}{space 1}   -0.94{col 46}{space 3}0.348{col 54}{space 4}-.1403196{col 67}{space 3} .0495655
{txt}{space 9}50  {c |}{col 14}{res}{space 2}-.0997742{col 26}{space 2} .0514555{col 37}{space 1}   -1.94{col 46}{space 3}0.054{col 54}{space 4}-.2010646{col 67}{space 3} .0015161
{txt}{space 9}51  {c |}{col 14}{res}{space 2}  .140717{col 26}{space 2} .0906788{col 37}{space 1}    1.55{col 46}{space 3}0.122{col 54}{space 4}-.0377845{col 67}{space 3} .3192184
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .0211618{col 26}{space 2} .0939042{col 37}{space 1}    0.23{col 46}{space 3}0.822{col 54}{space 4} -.163689{col 67}{space 3} .2060126
{txt}{space 9}53  {c |}{col 14}{res}{space 2}  .053113{col 26}{space 2} .0833881{col 37}{space 1}    0.64{col 46}{space 3}0.525{col 54}{space 4}-.1110368{col 67}{space 3} .2172628
{txt}{space 9}54  {c |}{col 14}{res}{space 2}-.0526025{col 26}{space 2} .0740424{col 37}{space 1}   -0.71{col 46}{space 3}0.478{col 54}{space 4}-.1983553{col 67}{space 3} .0931503
{txt}{space 9}55  {c |}{col 14}{res}{space 2}-.0843984{col 26}{space 2} .0856467{col 37}{space 1}   -0.99{col 46}{space 3}0.325{col 54}{space 4}-.2529942{col 67}{space 3} .0841973
{txt}{space 9}56  {c |}{col 14}{res}{space 2}-.1981986{col 26}{space 2} .0844419{col 37}{space 1}   -2.35{col 46}{space 3}0.020{col 54}{space 4}-.3644227{col 67}{space 3}-.0319744
{txt}{space 9}57  {c |}{col 14}{res}{space 2}-.0558308{col 26}{space 2} .1259126{col 37}{space 1}   -0.44{col 46}{space 3}0.658{col 54}{space 4}-.3036901{col 67}{space 3} .1920286
{txt}{space 9}58  {c |}{col 14}{res}{space 2}-.2215209{col 26}{space 2}  .068257{col 37}{space 1}   -3.25{col 46}{space 3}0.001{col 54}{space 4}-.3558849{col 67}{space 3}-.0871569
{txt}{space 9}59  {c |}{col 14}{res}{space 2}-.1707436{col 26}{space 2}  .105171{col 37}{space 1}   -1.62{col 46}{space 3}0.106{col 54}{space 4} -.377773{col 67}{space 3} .0362858
{txt}{space 9}60  {c |}{col 14}{res}{space 2}-.2851575{col 26}{space 2} .1057031{col 37}{space 1}   -2.70{col 46}{space 3}0.007{col 54}{space 4}-.4932343{col 67}{space 3}-.0770806
{txt}{space 9}61  {c |}{col 14}{res}{space 2}-.0948512{col 26}{space 2} .1098143{col 37}{space 1}   -0.86{col 46}{space 3}0.388{col 54}{space 4} -.311021{col 67}{space 3} .1213186
{txt}{space 9}62  {c |}{col 14}{res}{space 2}-.1121684{col 26}{space 2} .1176135{col 37}{space 1}   -0.95{col 46}{space 3}0.341{col 54}{space 4}-.3436909{col 67}{space 3} .1193541
{txt}{space 9}63  {c |}{col 14}{res}{space 2}-.2735803{col 26}{space 2} .0614033{col 37}{space 1}   -4.46{col 46}{space 3}0.000{col 54}{space 4}-.3944528{col 67}{space 3}-.1527078
{txt}{space 9}64  {c |}{col 14}{res}{space 2}-.1972565{col 26}{space 2} .0426317{col 37}{space 1}   -4.63{col 46}{space 3}0.000{col 54}{space 4}-.2811771{col 67}{space 3} -.113336
{txt}{space 9}65  {c |}{col 14}{res}{space 2}-.2259726{col 26}{space 2} .0241335{col 37}{space 1}   -9.36{col 46}{space 3}0.000{col 54}{space 4}-.2734794{col 67}{space 3}-.1784657
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .1590756{col 26}{space 2}  .121236{col 37}{space 1}    1.31{col 46}{space 3}0.191{col 54}{space 4}-.0795778{col 67}{space 3}  .397729
{txt}{space 9}67  {c |}{col 14}{res}{space 2}-.0682457{col 26}{space 2} .1230476{col 37}{space 1}   -0.55{col 46}{space 3}0.580{col 54}{space 4}-.3104652{col 67}{space 3} .1739739
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .1020464{col 26}{space 2} .0931942{col 37}{space 1}    1.09{col 46}{space 3}0.274{col 54}{space 4}-.0814066{col 67}{space 3} .2854995
{txt}{space 9}69  {c |}{col 14}{res}{space 2}-.0043688{col 26}{space 2} .0960465{col 37}{space 1}   -0.05{col 46}{space 3}0.964{col 54}{space 4}-.1934366{col 67}{space 3} .1846991
{txt}{space 9}70  {c |}{col 14}{res}{space 2}-.0585155{col 26}{space 2} .0830392{col 37}{space 1}   -0.70{col 46}{space 3}0.482{col 54}{space 4}-.2219784{col 67}{space 3} .1049473
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0545799{col 26}{space 2} .0477909{col 37}{space 1}    1.14{col 46}{space 3}0.254{col 54}{space 4}-.0394966{col 67}{space 3} .1486565
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_alt_cum>0.25 & voted_coethnics_alt_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,288
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       847
{txt}{col 49}{help j_robustsingular##|_new:F(  76,    281)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1533
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0989
{txt}{col 49}Root MSE{col 67}= {res}    0.4746

{txt}{ralign 78:(Std. Err. adjusted for {res:282} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} -.008206{col 26}{space 2} .0130108{col 37}{space 1}   -0.63{col 46}{space 3}0.529{col 54}{space 4}-.0338171{col 67}{space 3}  .017405
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0200477{col 26}{space 2}   .01156{col 37}{space 1}    1.73{col 46}{space 3}0.084{col 54}{space 4}-.0027075{col 67}{space 3}  .042803
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{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002478{col 26}{space 2}  .000024{col 37}{space 1}  -10.31{col 46}{space 3}0.000{col 54}{space 4}-.0002951{col 67}{space 3}-.0002005
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{txt}{space 6}parent {c |}{col 14}{res}{space 2}  .049229{col 26}{space 2} .0172165{col 37}{space 1}    2.86{col 46}{space 3}0.005{col 54}{space 4} .0153394{col 67}{space 3} .0831186
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0075211{col 26}{space 2} .0070079{col 37}{space 1}    1.07{col 46}{space 3}0.284{col 54}{space 4}-.0062734{col 67}{space 3} .0213157
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0819187{col 26}{space 2} .0111154{col 37}{space 1}    7.37{col 46}{space 3}0.000{col 54}{space 4} .0600388{col 67}{space 3} .1037987
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.2659242{col 26}{space 2} .1440084{col 37}{space 1}   -1.85{col 46}{space 3}0.066{col 54}{space 4}-.5493965{col 67}{space 3}  .017548
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{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0789213{col 26}{space 2} .2961733{col 37}{space 1}    0.27{col 46}{space 3}0.790{col 54}{space 4}-.5040786{col 67}{space 3} .6619213
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0870472{col 26}{space 2} .2124053{col 37}{space 1}   -0.41{col 46}{space 3}0.682{col 54}{space 4}-.5051547{col 67}{space 3} .3310603
{txt}{space 10}6  {c |}{col 14}{res}{space 2}   .08374{col 26}{space 2} .1554512{col 37}{space 1}    0.54{col 46}{space 3}0.591{col 54}{space 4}-.2222567{col 67}{space 3} .3897367
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{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0192599{col 26}{space 2} .1511408{col 37}{space 1}   -0.13{col 46}{space 3}0.899{col 54}{space 4}-.3167719{col 67}{space 3}  .278252
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0073182{col 26}{space 2} .1649388{col 37}{space 1}    0.04{col 46}{space 3}0.965{col 54}{space 4}-.3173543{col 67}{space 3} .3319908
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{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4419886{col 26}{space 2} .1863516{col 37}{space 1}    2.37{col 46}{space 3}0.018{col 54}{space 4} .0751662{col 67}{space 3}  .808811
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{txt}{space 9}14  {c |}{col 14}{res}{space 2} .2785359{col 26}{space 2}  .152059{col 37}{space 1}    1.83{col 46}{space 3}0.068{col 54}{space 4}-.0207835{col 67}{space 3} .5778552
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{txt}{space 9}20  {c |}{col 14}{res}{space 2}  .688921{col 26}{space 2} .1861721{col 37}{space 1}    3.70{col 46}{space 3}0.000{col 54}{space 4} .3224519{col 67}{space 3}  1.05539
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .4051641{col 26}{space 2} .1710681{col 37}{space 1}    2.37{col 46}{space 3}0.019{col 54}{space 4} .0684263{col 67}{space 3} .7419018
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{txt}{space 9}23  {c |}{col 14}{res}{space 2} .2202615{col 26}{space 2} .1872846{col 37}{space 1}    1.18{col 46}{space 3}0.241{col 54}{space 4}-.1483974{col 67}{space 3} .5889204
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{txt}{space 9}28  {c |}{col 14}{res}{space 2} .1586044{col 26}{space 2} .1578349{col 37}{space 1}    1.00{col 46}{space 3}0.316{col 54}{space 4}-.1520844{col 67}{space 3} .4692933
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{txt}{space 9}42  {c |}{col 14}{res}{space 2} .2923864{col 26}{space 2} .1527415{col 37}{space 1}    1.91{col 46}{space 3}0.057{col 54}{space 4}-.0082765{col 67}{space 3} .5930492
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{txt}{space 9}45  {c |}{col 14}{res}{space 2} .1487856{col 26}{space 2} .1412166{col 37}{space 1}    1.05{col 46}{space 3}0.293{col 54}{space 4} -.129191{col 67}{space 3} .4267622
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .2941151{col 26}{space 2}  .139831{col 37}{space 1}    2.10{col 46}{space 3}0.036{col 54}{space 4} .0188659{col 67}{space 3} .5693644
{txt}{space 9}47  {c |}{col 14}{res}{space 2}  .270048{col 26}{space 2} .1332672{col 37}{space 1}    2.03{col 46}{space 3}0.044{col 54}{space 4} .0077191{col 67}{space 3} .5323768
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .2885852{col 26}{space 2} .1540786{col 37}{space 1}    1.87{col 46}{space 3}0.062{col 54}{space 4}-.0147096{col 67}{space 3}   .59188
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .1852991{col 26}{space 2} .1521705{col 37}{space 1}    1.22{col 46}{space 3}0.224{col 54}{space 4}-.1142398{col 67}{space 3}  .484838
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .1806401{col 26}{space 2} .1421751{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}-.0992234{col 67}{space 3} .4605035
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .3928683{col 26}{space 2} .1770066{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4} .0444411{col 67}{space 3} .7412955
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{txt}{space 9}53  {c |}{col 14}{res}{space 2} .1774094{col 26}{space 2} .1653711{col 37}{space 1}    1.07{col 46}{space 3}0.284{col 54}{space 4}-.1481141{col 67}{space 3} .5029329
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{txt}{space 9}64  {c |}{col 14}{res}{space 2}   .03457{col 26}{space 2}  .160761{col 37}{space 1}    0.22{col 46}{space 3}0.830{col 54}{space 4}-.2818787{col 67}{space 3} .3510188
{txt}{space 9}65  {c |}{col 14}{res}{space 2}-.0159154{col 26}{space 2} .1516383{col 37}{space 1}   -0.10{col 46}{space 3}0.916{col 54}{space 4}-.3144067{col 67}{space 3} .2825758
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{txt}{space 9}67  {c |}{col 14}{res}{space 2} .3089064{col 26}{space 2} .1802915{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4} -.045987{col 67}{space 3} .6637997
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .3817234{col 26}{space 2} .1680121{col 37}{space 1}    2.27{col 46}{space 3}0.024{col 54}{space 4} .0510014{col 67}{space 3} .7124454
{txt}{space 9}69  {c |}{col 14}{res}{space 2}  .277219{col 26}{space 2}  .173572{col 37}{space 1}    1.60{col 46}{space 3}0.111{col 54}{space 4}-.0644475{col 67}{space 3} .6188855
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .2578809{col 26}{space 2} .1715983{col 37}{space 1}    1.50{col 46}{space 3}0.134{col 54}{space 4}-.0799003{col 67}{space 3} .5956622
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1395484{col 26}{space 2} .1603195{col 37}{space 1}   -0.87{col 46}{space 3}0.385{col 54}{space 4} -.455128{col 67}{space 3} .1760313
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_alt_cum>0.50 & voted_coethnics_alt_cum<0.75, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,240
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       331
{txt}{col 49}{help j_robustsingular##|_new:F(  72,    140)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1132
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0890
{txt}{col 49}Root MSE{col 67}= {res}    0.4760

{txt}{ralign 78:(Std. Err. adjusted for {res:141} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0069543{col 26}{space 2}  .009852{col 37}{space 1}    0.71{col 46}{space 3}0.481{col 54}{space 4}-.0125236{col 67}{space 3} .0264322
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0087125{col 26}{space 2}  .005167{col 37}{space 1}    1.69{col 46}{space 3}0.094{col 54}{space 4} -.001503{col 67}{space 3}  .018928
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0161997{col 26}{space 2} .0016244{col 37}{space 1}    9.97{col 46}{space 3}0.000{col 54}{space 4} .0129881{col 67}{space 3} .0194112
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001852{col 26}{space 2} .0000172{col 37}{space 1}  -10.76{col 46}{space 3}0.000{col 54}{space 4}-.0002192{col 67}{space 3}-.0001512
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0024441{col 26}{space 2} .0095825{col 37}{space 1}    0.26{col 46}{space 3}0.799{col 54}{space 4} -.016501{col 67}{space 3} .0213893
{txt}{space 6}parent {c |}{col 14}{res}{space 2}  .048872{col 26}{space 2} .0173818{col 37}{space 1}    2.81{col 46}{space 3}0.006{col 54}{space 4} .0145072{col 67}{space 3} .0832368
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0307672{col 26}{space 2} .0067081{col 37}{space 1}    4.59{col 46}{space 3}0.000{col 54}{space 4} .0175049{col 67}{space 3} .0440295
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0846594{col 26}{space 2} .0075989{col 37}{space 1}   11.14{col 46}{space 3}0.000{col 54}{space 4} .0696358{col 67}{space 3} .0996829
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 1.136518{col 26}{space 2} .0261231{col 37}{space 1}   43.51{col 46}{space 3}0.000{col 54}{space 4} 1.084871{col 67}{space 3} 1.188164
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0339619{col 26}{space 2} .0193875{col 37}{space 1}    1.75{col 46}{space 3}0.082{col 54}{space 4}-.0043682{col 67}{space 3}  .072292
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0007259{col 26}{space 2} .0505837{col 37}{space 1}    0.01{col 46}{space 3}0.989{col 54}{space 4}-.0992808{col 67}{space 3} .1007326
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .4634869{col 26}{space 2} .0342445{col 37}{space 1}   13.53{col 46}{space 3}0.000{col 54}{space 4} .3957837{col 67}{space 3} .5311902
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .5245515{col 26}{space 2} .0429209{col 37}{space 1}   12.22{col 46}{space 3}0.000{col 54}{space 4} .4396946{col 67}{space 3} .6094084
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .4294574{col 26}{space 2}  .035867{col 37}{space 1}   11.97{col 46}{space 3}0.000{col 54}{space 4} .3585464{col 67}{space 3} .5003684
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3913097{col 26}{space 2}  .031532{col 37}{space 1}   12.41{col 46}{space 3}0.000{col 54}{space 4} .3289692{col 67}{space 3} .4536502
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{txt}{space 9}70  {c |}{col 14}{res}{space 2} .5568624{col 26}{space 2} .0628159{col 37}{space 1}    8.86{col 46}{space 3}0.000{col 54}{space 4}  .432672{col 67}{space 3} .6810529
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4753875{col 26}{space 2} .0563237{col 37}{space 1}   -8.44{col 46}{space 3}0.000{col 54}{space 4}-.5867425{col 67}{space 3}-.3640326
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_alt_cum>0.75 & voted_coethnics_alt_cum!=., absorb(Forsamling) vce(cluster Kommun)
{txt}note: 2.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,286
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       546
{txt}{col 49}{help j_robustsingular##|_new:F(  75,    222)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1311
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0943
{txt}{col 49}Root MSE{col 67}= {res}    0.4742

{txt}{ralign 78:(Std. Err. adjusted for {res:223} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0280386{col 26}{space 2} .0108486{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4} .0066593{col 67}{space 3}  .049418
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0104953{col 26}{space 2} .0074537{col 37}{space 1}    1.41{col 46}{space 3}0.161{col 54}{space 4}-.0041938{col 67}{space 3} .0251844
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0192576{col 26}{space 2} .0018889{col 37}{space 1}   10.20{col 46}{space 3}0.000{col 54}{space 4} .0155352{col 67}{space 3}   .02298
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002264{col 26}{space 2} .0000221{col 37}{space 1}  -10.26{col 46}{space 3}0.000{col 54}{space 4}-.0002699{col 67}{space 3}-.0001829
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0115156{col 26}{space 2} .0088144{col 37}{space 1}    1.31{col 46}{space 3}0.193{col 54}{space 4}-.0058551{col 67}{space 3} .0288863
{txt}{space 6}parent {c |}{col 14}{res}{space 2}  .067307{col 26}{space 2}  .019772{col 37}{space 1}    3.40{col 46}{space 3}0.001{col 54}{space 4} .0283421{col 67}{space 3} .1062718
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0082006{col 26}{space 2} .0077764{col 37}{space 1}    1.05{col 46}{space 3}0.293{col 54}{space 4}-.0071243{col 67}{space 3} .0235255
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0871696{col 26}{space 2} .0115323{col 37}{space 1}    7.56{col 46}{space 3}0.000{col 54}{space 4} .0644428{col 67}{space 3} .1098964
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}3  {c |}{col 14}{res}{space 2}-.0781518{col 26}{space 2} .2835048{col 37}{space 1}   -0.28{col 46}{space 3}0.783{col 54}{space 4}-.6368568{col 67}{space 3} .4805531
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.4570411{col 26}{space 2}  .181739{col 37}{space 1}   -2.51{col 46}{space 3}0.013{col 54}{space 4}-.8151955{col 67}{space 3}-.0988868
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.3656275{col 26}{space 2} .1825624{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-.7254046{col 67}{space 3}-.0058505
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.2173956{col 26}{space 2} .1813547{col 37}{space 1}   -1.20{col 46}{space 3}0.232{col 54}{space 4}-.5747926{col 67}{space 3} .1400015
{txt}{space 10}7  {c |}{col 14}{res}{space 2} -.183934{col 26}{space 2} .2052636{col 37}{space 1}   -0.90{col 46}{space 3}0.371{col 54}{space 4}-.5884484{col 67}{space 3} .2205805
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0094859{col 26}{space 2} .2001961{col 37}{space 1}    0.05{col 46}{space 3}0.962{col 54}{space 4} -.385042{col 67}{space 3} .4040138
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.1561568{col 26}{space 2} .1837967{col 37}{space 1}   -0.85{col 46}{space 3}0.396{col 54}{space 4}-.5183663{col 67}{space 3} .2060526
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0007548{col 26}{space 2} .2010012{col 37}{space 1}    0.00{col 46}{space 3}0.997{col 54}{space 4}-.3953599{col 67}{space 3} .3968694
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .3150669{col 26}{space 2} .1999373{col 37}{space 1}    1.58{col 46}{space 3}0.116{col 54}{space 4} -.078951{col 67}{space 3} .7090848
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .2159253{col 26}{space 2} .1957971{col 37}{space 1}    1.10{col 46}{space 3}0.271{col 54}{space 4}-.1699335{col 67}{space 3} .6017842
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .2435768{col 26}{space 2} .1828041{col 37}{space 1}    1.33{col 46}{space 3}0.184{col 54}{space 4}-.1166765{col 67}{space 3} .6038301
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .2900526{col 26}{space 2} .1482879{col 37}{space 1}    1.96{col 46}{space 3}0.052{col 54}{space 4}-.0021795{col 67}{space 3} .5822848
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .2263191{col 26}{space 2} .1595927{col 37}{space 1}    1.42{col 46}{space 3}0.158{col 54}{space 4}-.0881914{col 67}{space 3} .5408296
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .3043043{col 26}{space 2} .1807143{col 37}{space 1}    1.68{col 46}{space 3}0.094{col 54}{space 4}-.0518307{col 67}{space 3} .6604393
{txt}{space 9}17  {c |}{col 14}{res}{space 2}  .310336{col 26}{space 2} .1756655{col 37}{space 1}    1.77{col 46}{space 3}0.079{col 54}{space 4}-.0358492{col 67}{space 3} .6565212
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .3044393{col 26}{space 2} .1801541{col 37}{space 1}    1.69{col 46}{space 3}0.092{col 54}{space 4}-.0505918{col 67}{space 3} .6594703
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .1977142{col 26}{space 2} .1918233{col 37}{space 1}    1.03{col 46}{space 3}0.304{col 54}{space 4}-.1803134{col 67}{space 3} .5757418
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{txt}{space 9}21  {c |}{col 14}{res}{space 2} .2262772{col 26}{space 2} .1839419{col 37}{space 1}    1.23{col 46}{space 3}0.220{col 54}{space 4}-.1362184{col 67}{space 3} .5887727
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .1621003{col 26}{space 2} .1822303{col 37}{space 1}    0.89{col 46}{space 3}0.375{col 54}{space 4}-.1970222{col 67}{space 3} .5212228
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .2680248{col 26}{space 2} .1762519{col 37}{space 1}    1.52{col 46}{space 3}0.130{col 54}{space 4} -.079316{col 67}{space 3} .6153657
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .2248809{col 26}{space 2} .1802761{col 37}{space 1}    1.25{col 46}{space 3}0.214{col 54}{space 4}-.1303904{col 67}{space 3} .5801523
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .1876499{col 26}{space 2} .1764202{col 37}{space 1}    1.06{col 46}{space 3}0.289{col 54}{space 4}-.1600226{col 67}{space 3} .5353225
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{txt}{space 9}68  {c |}{col 14}{res}{space 2}  .228917{col 26}{space 2} .1727253{col 37}{space 1}    1.33{col 46}{space 3}0.186{col 54}{space 4}-.1114741{col 67}{space 3}  .569308
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .1368265{col 26}{space 2} .1782667{col 37}{space 1}    0.77{col 46}{space 3}0.444{col 54}{space 4} -.214485{col 67}{space 3} .4881379
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .1293409{col 26}{space 2} .1804854{col 37}{space 1}    0.72{col 46}{space 3}0.474{col 54}{space 4}-.2263429{col 67}{space 3} .4850248
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.0565853{col 26}{space 2} .1779257{col 37}{space 1}   -0.32{col 46}{space 3}0.751{col 54}{space 4}-.4072248{col 67}{space 3} .2940542
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_alt.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_alt.tex"'})

{com}. 
. 
.  ///LONG TERM RESULTS
>  
.  preserve
{txt}
{com}.  ///long term: MERGE ON 2010 VOTING DATA
>  
. rename r r_first
{res}{txt}
{com}. rename k k_first
{res}{txt}
{com}. rename l l_first
{res}{txt}
{com}.  
.  
. merge 1:m LopNr using "D:\SCB_ConPol\Stata\VD\Valdelt_2010", keepusing (LopNr r k l)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,213,376
{txt}{col 9}from master{col 30}{res}          11,355{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,202,021{txt}  (_merge==2)

{col 5}matched{col 30}{res}          52,018{txt}  (_merge==3)
{col 5}{hline 41}

{com}. keep if _merge==3
{txt}(7,213,376 observations deleted)

{com}. 
. 
. *check for duplicates - OK
. *duplicates report LopNr 
. 
. ///create voting variable
> 
. ///voted or not
> gen voted_k2010 =0 if k==1
{txt}(36,940 missing values generated)

{com}. replace voted_k2010=1 if voted_k2010==.
{txt}(36,940 real changes made)

{com}. 
. label variable voted_k2010  "voted in local election 2010"
{txt}
{com}. 
. 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    49,990
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}     1,006
{txt}{col 49}F({res}  77{txt},{res}    284{txt}){col 67}= {res}     65.48
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0695
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0490
{txt}{col 49}Root MSE{col 67}= {res}    0.4414

{txt}{ralign 78:(Std. Err. adjusted for {res:285} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0022564{col 26}{space 2} .0035621{col 37}{space 1}   -0.63{col 46}{space 3}0.527{col 54}{space 4}-.0092678{col 67}{space 3} .0047551
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0268812{col 26}{space 2} .0054461{col 37}{space 1}   -4.94{col 46}{space 3}0.000{col 54}{space 4}-.0376011{col 67}{space 3}-.0161613
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0147823{col 26}{space 2} .0011478{col 37}{space 1}   12.88{col 46}{space 3}0.000{col 54}{space 4} .0125231{col 67}{space 3} .0170415
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002483{col 26}{space 2} .0000138{col 37}{space 1}  -18.03{col 46}{space 3}0.000{col 54}{space 4}-.0002754{col 67}{space 3}-.0002212
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{txt}{space 4}children {c |}{col 14}{res}{space 2} .0017996{col 26}{space 2} .0023272{col 37}{space 1}    0.77{col 46}{space 3}0.440{col 54}{space 4}-.0027812{col 67}{space 3} .0063804
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0523057{col 26}{space 2}  .005624{col 37}{space 1}    9.30{col 46}{space 3}0.000{col 54}{space 4} .0412358{col 67}{space 3} .0633757
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}67  {c |}{col 14}{res}{space 2} .4176677{col 26}{space 2} .1476846{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .1269724{col 67}{space 3}  .708363
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{txt}{space 9}69  {c |}{col 14}{res}{space 2} .4990939{col 26}{space 2} .1434562{col 37}{space 1}    3.48{col 46}{space 3}0.001{col 54}{space 4} .2167217{col 67}{space 3} .7814662
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4372776{col 26}{space 2} .1671182{col 37}{space 1}    2.62{col 46}{space 3}0.009{col 54}{space 4} .1083301{col 67}{space 3}  .766225
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .1108516{col 26}{space 2} .1442784{col 37}{space 1}    0.77{col 46}{space 3}0.443{col 54}{space 4}-.1731391{col 67}{space 3} .3948423
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry if voted_coethnics_alt_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 5.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    13,179
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       846
{txt}{col 49}{help j_robustsingular##|_new:F(  74,    278)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1044
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0371
{txt}{col 49}Root MSE{col 67}= {res}    0.4356

{txt}{ralign 78:(Std. Err. adjusted for {res:279} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0058594{col 26}{space 2} .0089784{col 37}{space 1}    0.65{col 46}{space 3}0.515{col 54}{space 4}-.0118149{col 67}{space 3} .0235336
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0365765{col 26}{space 2}   .00924{col 37}{space 1}   -3.96{col 46}{space 3}0.000{col 54}{space 4}-.0547658{col 67}{space 3}-.0183873
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0173568{col 26}{space 2} .0025868{col 37}{space 1}    6.71{col 46}{space 3}0.000{col 54}{space 4} .0122646{col 67}{space 3}  .022449
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002777{col 26}{space 2} .0000345{col 37}{space 1}   -8.05{col 46}{space 3}0.000{col 54}{space 4}-.0003455{col 67}{space 3}-.0002098
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0105111{col 26}{space 2} .0121333{col 37}{space 1}    0.87{col 46}{space 3}0.387{col 54}{space 4}-.0133738{col 67}{space 3} .0343959
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0298195{col 26}{space 2} .0153613{col 37}{space 1}    1.94{col 46}{space 3}0.053{col 54}{space 4}-.0004197{col 67}{space 3} .0600587
{txt}{space 4}children {c |}{col 14}{res}{space 2} -.004272{col 26}{space 2} .0046853{col 37}{space 1}   -0.91{col 46}{space 3}0.363{col 54}{space 4}-.0134951{col 67}{space 3} .0049512
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0305796{col 26}{space 2} .0103814{col 37}{space 1}    2.95{col 46}{space 3}0.003{col 54}{space 4} .0101434{col 67}{space 3} .0510157
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 10}4  {c |}{col 14}{res}{space 2} 1.527131{col 26}{space 2} .0754797{col 37}{space 1}   20.23{col 46}{space 3}0.000{col 54}{space 4} 1.378546{col 67}{space 3} 1.675715
{txt}{space 10}5  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
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{txt}{space 10}9  {c |}{col 14}{res}{space 2} .7154769{col 26}{space 2} .1192352{col 37}{space 1}    6.00{col 46}{space 3}0.000{col 54}{space 4} .4807583{col 67}{space 3} .9501956
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .6225941{col 26}{space 2} .1346961{col 37}{space 1}    4.62{col 46}{space 3}0.000{col 54}{space 4} .3574403{col 67}{space 3}  .887748
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .8767204{col 26}{space 2} .0770316{col 37}{space 1}   11.38{col 46}{space 3}0.000{col 54}{space 4} .7250811{col 67}{space 3}  1.02836
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3104019{col 26}{space 2} .0630017{col 37}{space 1}   -4.93{col 46}{space 3}0.000{col 54}{space 4} -.434423{col 67}{space 3}-.1863809
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry if voted_coethnics_alt_cum>0.25 & voted_coethnics_alt_cum<0.50, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    12,574
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       811
{txt}{col 49}{help j_robustsingular##|_new:F(  76,    280)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1204
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0537
{txt}{col 49}Root MSE{col 67}= {res}    0.4380

{txt}{ralign 78:(Std. Err. adjusted for {res:281} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0132748{col 26}{space 2} .0118802{col 37}{space 1}    1.12{col 46}{space 3}0.265{col 54}{space 4}-.0101111{col 67}{space 3} .0366607
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0340451{col 26}{space 2} .0091805{col 37}{space 1}   -3.71{col 46}{space 3}0.000{col 54}{space 4}-.0521167{col 67}{space 3}-.0159736
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0150671{col 26}{space 2} .0023023{col 37}{space 1}    6.54{col 46}{space 3}0.000{col 54}{space 4}  .010535{col 67}{space 3} .0195991
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002572{col 26}{space 2} .0000276{col 37}{space 1}   -9.31{col 46}{space 3}0.000{col 54}{space 4}-.0003116{col 67}{space 3}-.0002028
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0111252{col 26}{space 2} .0116507{col 37}{space 1}    0.95{col 46}{space 3}0.340{col 54}{space 4}-.0118089{col 67}{space 3} .0340593
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{txt}{space 4}children {c |}{col 14}{res}{space 2}-.0030782{col 26}{space 2} .0055859{col 37}{space 1}   -0.55{col 46}{space 3}0.582{col 54}{space 4}-.0140739{col 67}{space 3} .0079175
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{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}68  {c |}{col 14}{res}{space 2} .5663724{col 26}{space 2} .2804879{col 37}{space 1}    2.02{col 46}{space 3}0.044{col 54}{space 4} .0142398{col 67}{space 3} 1.118505
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6529128{col 26}{space 2} .2970085{col 37}{space 1}    2.20{col 46}{space 3}0.029{col 54}{space 4} .0682598{col 67}{space 3} 1.237566
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .6083267{col 26}{space 2} .2917675{col 37}{space 1}    2.08{col 46}{space 3}0.038{col 54}{space 4} .0339904{col 67}{space 3} 1.182663
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.0185279{col 26}{space 2}  .276276{col 37}{space 1}   -0.07{col 46}{space 3}0.947{col 54}{space 4}-.5623696{col 67}{space 3} .5253138
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry if voted_coethnics_alt_cum>0.50 & voted_coethnics_alt_cum<0.75, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    11,744
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       315
{txt}{col 49}{help j_robustsingular##|_new:F(  72,    135)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1005
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0696
{txt}{col 49}Root MSE{col 67}= {res}    0.4496

{txt}{ralign 78:(Std. Err. adjusted for {res:136} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0096631{col 26}{space 2} .0050791{col 37}{space 1}   -1.90{col 46}{space 3}0.059{col 54}{space 4} -.019708{col 67}{space 3} .0003818
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0214088{col 26}{space 2} .0137998{col 37}{space 1}   -1.55{col 46}{space 3}0.123{col 54}{space 4}-.0487005{col 67}{space 3} .0058829
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0135107{col 26}{space 2} .0020083{col 37}{space 1}    6.73{col 46}{space 3}0.000{col 54}{space 4}  .009539{col 67}{space 3} .0174825
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002338{col 26}{space 2} .0000241{col 37}{space 1}   -9.69{col 46}{space 3}0.000{col 54}{space 4}-.0002815{col 67}{space 3}-.0001861
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0044681{col 26}{space 2} .0088345{col 37}{space 1}    0.51{col 46}{space 3}0.614{col 54}{space 4}-.0130038{col 67}{space 3} .0219401
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0248289{col 26}{space 2}  .014151{col 37}{space 1}    1.75{col 46}{space 3}0.082{col 54}{space 4}-.0031574{col 67}{space 3} .0528153
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0046542{col 26}{space 2} .0060463{col 37}{space 1}    0.77{col 46}{space 3}0.443{col 54}{space 4}-.0073035{col 67}{space 3} .0166119
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0592416{col 26}{space 2} .0081747{col 37}{space 1}    7.25{col 46}{space 3}0.000{col 54}{space 4} .0430746{col 67}{space 3} .0754085
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .5806016{col 26}{space 2} .0150717{col 37}{space 1}   38.52{col 46}{space 3}0.000{col 54}{space 4} .5507945{col 67}{space 3} .6104087
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.4788238{col 26}{space 2} .0087848{col 37}{space 1}  -54.51{col 46}{space 3}0.000{col 54}{space 4}-.4961974{col 67}{space 3}-.4614502
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.6178732{col 26}{space 2} .0287057{col 37}{space 1}  -21.52{col 46}{space 3}0.000{col 54}{space 4}-.6746442{col 67}{space 3}-.5611022
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0281141{col 26}{space 2} .0355914{col 37}{space 1}   -0.79{col 46}{space 3}0.431{col 54}{space 4} -.098503{col 67}{space 3} .0422748
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{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0875681{col 26}{space 2} .0301053{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4}  .028029{col 67}{space 3} .1471072
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1011512{col 26}{space 2} .0234949{col 37}{space 1}    4.31{col 46}{space 3}0.000{col 54}{space 4} .0546855{col 67}{space 3} .1476168
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0612774{col 26}{space 2} .0344974{col 37}{space 1}    1.78{col 46}{space 3}0.078{col 54}{space 4}-.0069478{col 67}{space 3} .1295027
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1317289{col 26}{space 2} .0923522{col 37}{space 1}    1.43{col 46}{space 3}0.156{col 54}{space 4}-.0509152{col 67}{space 3} .3143731
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5365787{col 26}{space 2} .0411047{col 37}{space 1}   13.05{col 46}{space 3}0.000{col 54}{space 4} .4552863{col 67}{space 3} .6178711
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3573806{col 26}{space 2}  .130131{col 37}{space 1}    2.75{col 46}{space 3}0.007{col 54}{space 4} .1000216{col 67}{space 3} .6147396
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{txt}{space 9}15  {c |}{col 14}{res}{space 2} .4404818{col 26}{space 2} .0892242{col 37}{space 1}    4.94{col 46}{space 3}0.000{col 54}{space 4} .2640238{col 67}{space 3} .6169399
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .2350814{col 26}{space 2} .0549767{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} .1263545{col 67}{space 3} .3438083
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .2138552{col 26}{space 2} .0500283{col 37}{space 1}    4.27{col 46}{space 3}0.000{col 54}{space 4} .1149147{col 67}{space 3} .3127957
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .2292105{col 26}{space 2}  .032588{col 37}{space 1}    7.03{col 46}{space 3}0.000{col 54}{space 4} .1647614{col 67}{space 3} .2936595
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{txt}{space 9}20  {c |}{col 14}{res}{space 2}  .201263{col 26}{space 2} .0951632{col 37}{space 1}    2.11{col 46}{space 3}0.036{col 54}{space 4} .0130594{col 67}{space 3} .3894666
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .2302389{col 26}{space 2} .0274249{col 37}{space 1}    8.40{col 46}{space 3}0.000{col 54}{space 4} .1760009{col 67}{space 3}  .284477
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .3305617{col 26}{space 2} .0277583{col 37}{space 1}   11.91{col 46}{space 3}0.000{col 54}{space 4} .2756643{col 67}{space 3} .3854592
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .3056014{col 26}{space 2} .0271515{col 37}{space 1}   11.26{col 46}{space 3}0.000{col 54}{space 4} .2519042{col 67}{space 3} .3592986
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{txt}{space 9}25  {c |}{col 14}{res}{space 2} .2933549{col 26}{space 2} .0360193{col 37}{space 1}    8.14{col 46}{space 3}0.000{col 54}{space 4} .2221198{col 67}{space 3}   .36459
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .2521311{col 26}{space 2} .0275462{col 37}{space 1}    9.15{col 46}{space 3}0.000{col 54}{space 4} .1976531{col 67}{space 3} .3066091
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .2918674{col 26}{space 2} .0340065{col 37}{space 1}    8.58{col 46}{space 3}0.000{col 54}{space 4} .2246131{col 67}{space 3} .3591217
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .1427012{col 26}{space 2} .0358165{col 37}{space 1}    3.98{col 46}{space 3}0.000{col 54}{space 4} .0718672{col 67}{space 3} .2135353
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .3398204{col 26}{space 2} .0275503{col 37}{space 1}   12.33{col 46}{space 3}0.000{col 54}{space 4} .2853344{col 67}{space 3} .3943065
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .2556979{col 26}{space 2} .0452977{col 37}{space 1}    5.64{col 46}{space 3}0.000{col 54}{space 4} .1661129{col 67}{space 3} .3452828
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .1827105{col 26}{space 2} .0262068{col 37}{space 1}    6.97{col 46}{space 3}0.000{col 54}{space 4} .1308815{col 67}{space 3} .2345394
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{txt}{space 9}40  {c |}{col 14}{res}{space 2} .2831701{col 26}{space 2} .0476454{col 37}{space 1}    5.94{col 46}{space 3}0.000{col 54}{space 4} .1889423{col 67}{space 3}  .377398
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{txt}{space 9}44  {c |}{col 14}{res}{space 2}  .222027{col 26}{space 2} .0346905{col 37}{space 1}    6.40{col 46}{space 3}0.000{col 54}{space 4} .1534198{col 67}{space 3} .2906342
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .2730623{col 26}{space 2} .0463645{col 37}{space 1}    5.89{col 46}{space 3}0.000{col 54}{space 4} .1813676{col 67}{space 3} .3647571
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .1353665{col 26}{space 2} .0578884{col 37}{space 1}    2.34{col 46}{space 3}0.021{col 54}{space 4} .0208811{col 67}{space 3} .2498518
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .1467043{col 26}{space 2} .0786921{col 37}{space 1}    1.86{col 46}{space 3}0.064{col 54}{space 4}-.0089246{col 67}{space 3} .3023331
{txt}{space 9}48  {c |}{col 14}{res}{space 2}  .219006{col 26}{space 2} .0412651{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4} .1373963{col 67}{space 3} .3006157
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .1902279{col 26}{space 2} .0380078{col 37}{space 1}    5.00{col 46}{space 3}0.000{col 54}{space 4} .1150602{col 67}{space 3} .2653955
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .3123898{col 26}{space 2}  .026138{col 37}{space 1}   11.95{col 46}{space 3}0.000{col 54}{space 4} .2606969{col 67}{space 3} .3640827
{txt}{space 9}51  {c |}{col 14}{res}{space 2}  .243758{col 26}{space 2} .0287828{col 37}{space 1}    8.47{col 46}{space 3}0.000{col 54}{space 4} .1868345{col 67}{space 3} .3006815
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .1548543{col 26}{space 2} .0416515{col 37}{space 1}    3.72{col 46}{space 3}0.000{col 54}{space 4} .0724805{col 67}{space 3} .2372281
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{txt}{space 9}54  {c |}{col 14}{res}{space 2} .2123355{col 26}{space 2} .0443534{col 37}{space 1}    4.79{col 46}{space 3}0.000{col 54}{space 4} .1246181{col 67}{space 3} .3000528
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .2083673{col 26}{space 2} .0466809{col 37}{space 1}    4.46{col 46}{space 3}0.000{col 54}{space 4} .1160469{col 67}{space 3} .3006877
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .1017773{col 26}{space 2} .0720579{col 37}{space 1}    1.41{col 46}{space 3}0.160{col 54}{space 4}-.0407311{col 67}{space 3} .2442857
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{txt}{space 9}61  {c |}{col 14}{res}{space 2}  .132253{col 26}{space 2} .0733728{col 37}{space 1}    1.80{col 46}{space 3}0.074{col 54}{space 4}-.0128559{col 67}{space 3}  .277362
{txt}{space 9}62  {c |}{col 14}{res}{space 2}-.0383515{col 26}{space 2} .0505408{col 37}{space 1}   -0.76{col 46}{space 3}0.449{col 54}{space 4}-.1383057{col 67}{space 3} .0616026
{txt}{space 9}63  {c |}{col 14}{res}{space 2}-.0278804{col 26}{space 2}  .041713{col 37}{space 1}   -0.67{col 46}{space 3}0.505{col 54}{space 4}-.1103758{col 67}{space 3}  .054615
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .0610891{col 26}{space 2} .0826742{col 37}{space 1}    0.74{col 46}{space 3}0.461{col 54}{space 4} -.102415{col 67}{space 3} .2245933
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .0503815{col 26}{space 2}  .049736{col 37}{space 1}    1.01{col 46}{space 3}0.313{col 54}{space 4} -.047981{col 67}{space 3} .1487439
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{txt}{space 9}67  {c |}{col 14}{res}{space 2} .3026724{col 26}{space 2} .0468879{col 37}{space 1}    6.46{col 46}{space 3}0.000{col 54}{space 4} .2099424{col 67}{space 3} .3954023
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .2065005{col 26}{space 2} .0601914{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} .0874604{col 67}{space 3} .3255407
{txt}{space 9}69  {c |}{col 14}{res}{space 2}   .32855{col 26}{space 2} .0369116{col 37}{space 1}    8.90{col 46}{space 3}0.000{col 54}{space 4} .2555503{col 67}{space 3} .4015497
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .3282081{col 26}{space 2} .0464131{col 37}{space 1}    7.07{col 46}{space 3}0.000{col 54}{space 4} .2364173{col 67}{space 3}  .419999
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .3190884{col 26}{space 2} .0422922{col 37}{space 1}    7.54{col 46}{space 3}0.000{col 54}{space 4} .2354474{col 67}{space 3} .4027295
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k2010 stdcoshare $cov i.cohortbycountry if voted_coethnics_alt_cum>0.75 & voted_coethnics_alt_cum!=., absorb(Forsamling) vce(cluster Kommun)
{txt}note: 2.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    12,493
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       519
{txt}{col 49}F({res}  76{txt},{res}    218{txt}){col 67}= {res}     19.11
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0967
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0516
{txt}{col 49}Root MSE{col 67}= {res}    0.4381

{txt}{ralign 78:(Std. Err. adjusted for {res:219} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0014373{col 26}{space 2} .0153183{col 37}{space 1}    0.09{col 46}{space 3}0.925{col 54}{space 4}-.0287537{col 67}{space 3} .0316283
{txt}{space 9}sex {c |}{col 14}{res}{space 2} -.024204{col 26}{space 2} .0082061{col 37}{space 1}   -2.95{col 46}{space 3}0.004{col 54}{space 4}-.0403774{col 67}{space 3}-.0080305
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .012166{col 26}{space 2} .0021673{col 37}{space 1}    5.61{col 46}{space 3}0.000{col 54}{space 4} .0078945{col 67}{space 3} .0164375
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002078{col 26}{space 2} .0000287{col 37}{space 1}   -7.24{col 46}{space 3}0.000{col 54}{space 4}-.0002643{col 67}{space 3}-.0001513
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0318106{col 26}{space 2} .0110659{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0100007{col 67}{space 3} .0536205
{txt}{space 6}parent {c |}{col 14}{res}{space 2}-.0035514{col 26}{space 2}  .017044{col 37}{space 1}   -0.21{col 46}{space 3}0.835{col 54}{space 4}-.0371434{col 67}{space 3} .0300407
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0059902{col 26}{space 2} .0054695{col 37}{space 1}    1.10{col 46}{space 3}0.275{col 54}{space 4}-.0047897{col 67}{space 3} .0167701
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0600431{col 26}{space 2} .0102857{col 37}{space 1}    5.84{col 46}{space 3}0.000{col 54}{space 4} .0397709{col 67}{space 3} .0803153
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}3  {c |}{col 14}{res}{space 2}-.0373936{col 26}{space 2}  .437292{col 37}{space 1}   -0.09{col 46}{space 3}0.932{col 54}{space 4}-.8992548{col 67}{space 3} .8244676
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0312414{col 26}{space 2} .4609566{col 37}{space 1}    0.07{col 46}{space 3}0.946{col 54}{space 4}-.8772605{col 67}{space 3} .9397433
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0841497{col 26}{space 2} .5574295{col 37}{space 1}   -0.15{col 46}{space 3}0.880{col 54}{space 4}-1.182791{col 67}{space 3} 1.014491
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{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.2470038{col 26}{space 2} .4290945{col 37}{space 1}   -0.58{col 46}{space 3}0.565{col 54}{space 4}-1.092709{col 67}{space 3}  .598701
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0342751{col 26}{space 2} .4575968{col 37}{space 1}    0.07{col 46}{space 3}0.940{col 54}{space 4} -.867605{col 67}{space 3} .9361551
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0371847{col 26}{space 2} .4649021{col 37}{space 1}   -0.08{col 46}{space 3}0.936{col 54}{space 4}-.9534628{col 67}{space 3} .8790934
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0929274{col 26}{space 2} .4622985{col 37}{space 1}   -0.20{col 46}{space 3}0.841{col 54}{space 4}-1.004074{col 67}{space 3} .8182193
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .2479944{col 26}{space 2} .4653652{col 37}{space 1}    0.53{col 46}{space 3}0.595{col 54}{space 4}-.6691965{col 67}{space 3} 1.165185
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0339796{col 26}{space 2} .4596747{col 37}{space 1}    0.07{col 46}{space 3}0.941{col 54}{space 4}-.8719959{col 67}{space 3}  .939955
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .0987743{col 26}{space 2}   .43639{col 37}{space 1}    0.23{col 46}{space 3}0.821{col 54}{space 4}-.7613091{col 67}{space 3} .9588577
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .2128522{col 26}{space 2} .4623551{col 37}{space 1}    0.46{col 46}{space 3}0.646{col 54}{space 4}-.6984062{col 67}{space 3}  1.12411
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .2519878{col 26}{space 2} .4597827{col 37}{space 1}    0.55{col 46}{space 3}0.584{col 54}{space 4}-.6542006{col 67}{space 3} 1.158176
{txt}{space 9}16  {c |}{col 14}{res}{space 2}  .128005{col 26}{space 2} .4592171{col 37}{space 1}    0.28{col 46}{space 3}0.781{col 54}{space 4}-.7770686{col 67}{space 3} 1.033079
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .1496146{col 26}{space 2} .4573225{col 37}{space 1}    0.33{col 46}{space 3}0.744{col 54}{space 4}-.7517249{col 67}{space 3} 1.050954
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .1263305{col 26}{space 2} .4615564{col 37}{space 1}    0.27{col 46}{space 3}0.785{col 54}{space 4}-.7833536{col 67}{space 3} 1.036015
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .0621908{col 26}{space 2} .4614361{col 37}{space 1}    0.13{col 46}{space 3}0.893{col 54}{space 4}-.8472562{col 67}{space 3} .9716379
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .0571744{col 26}{space 2} .4652975{col 37}{space 1}    0.12{col 46}{space 3}0.902{col 54}{space 4} -.859883{col 67}{space 3} .9742318
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .1583161{col 26}{space 2} .4559468{col 37}{space 1}    0.35{col 46}{space 3}0.729{col 54}{space 4}-.7403119{col 67}{space 3} 1.056944
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .2728223{col 26}{space 2} .4685974{col 37}{space 1}    0.58{col 46}{space 3}0.561{col 54}{space 4} -.650739{col 67}{space 3} 1.196384
{txt}{space 9}23  {c |}{col 14}{res}{space 2}  .198321{col 26}{space 2} .4595016{col 37}{space 1}    0.43{col 46}{space 3}0.666{col 54}{space 4}-.7073134{col 67}{space 3} 1.103955
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .1827331{col 26}{space 2} .4463439{col 37}{space 1}    0.41{col 46}{space 3}0.683{col 54}{space 4}-.6969686{col 67}{space 3} 1.062435
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .1382925{col 26}{space 2}  .456013{col 37}{space 1}    0.30{col 46}{space 3}0.762{col 54}{space 4}-.7604661{col 67}{space 3} 1.037051
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .2005873{col 26}{space 2} .4481378{col 37}{space 1}    0.45{col 46}{space 3}0.655{col 54}{space 4}-.6826501{col 67}{space 3} 1.083825
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .1516305{col 26}{space 2}  .454512{col 37}{space 1}    0.33{col 46}{space 3}0.739{col 54}{space 4}-.7441696{col 67}{space 3} 1.047431
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .1850448{col 26}{space 2} .4591277{col 37}{space 1}    0.40{col 46}{space 3}0.687{col 54}{space 4}-.7198526{col 67}{space 3} 1.089942
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .1875538{col 26}{space 2} .4561114{col 37}{space 1}    0.41{col 46}{space 3}0.681{col 54}{space 4}-.7113987{col 67}{space 3} 1.086506
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1509936{col 26}{space 2}  .446814{col 37}{space 1}    0.34{col 46}{space 3}0.736{col 54}{space 4}-.7296347{col 67}{space 3} 1.031622
{txt}{space 9}31  {c |}{col 14}{res}{space 2}  .054507{col 26}{space 2} .4528011{col 37}{space 1}    0.12{col 46}{space 3}0.904{col 54}{space 4}-.8379213{col 67}{space 3} .9469353
{txt}{space 9}32  {c |}{col 14}{res}{space 2}  .032252{col 26}{space 2}  .464075{col 37}{space 1}    0.07{col 46}{space 3}0.945{col 54}{space 4}-.8823961{col 67}{space 3}    .9469
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .0837701{col 26}{space 2}  .460125{col 37}{space 1}    0.18{col 46}{space 3}0.856{col 54}{space 4}-.8230928{col 67}{space 3} .9906331
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .0206537{col 26}{space 2} .4434162{col 37}{space 1}    0.05{col 46}{space 3}0.963{col 54}{space 4}-.8532777{col 67}{space 3} .8945851
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .0948147{col 26}{space 2} .4585999{col 37}{space 1}    0.21{col 46}{space 3}0.836{col 54}{space 4}-.8090424{col 67}{space 3} .9986719
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .0106637{col 26}{space 2} .4545806{col 37}{space 1}    0.02{col 46}{space 3}0.981{col 54}{space 4}-.8852718{col 67}{space 3} .9065992
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .1306774{col 26}{space 2} .4512898{col 37}{space 1}    0.29{col 46}{space 3}0.772{col 54}{space 4}-.7587722{col 67}{space 3} 1.020127
{txt}{space 9}38  {c |}{col 14}{res}{space 2}  .063931{col 26}{space 2} .4483159{col 37}{space 1}    0.14{col 46}{space 3}0.887{col 54}{space 4}-.8196574{col 67}{space 3} .9475194
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .0997622{col 26}{space 2} .4364411{col 37}{space 1}    0.23{col 46}{space 3}0.819{col 54}{space 4}-.7604219{col 67}{space 3} .9599464
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .0929295{col 26}{space 2} .4582689{col 37}{space 1}    0.20{col 46}{space 3}0.839{col 54}{space 4}-.8102753{col 67}{space 3} .9961343
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .1693833{col 26}{space 2} .4489474{col 37}{space 1}    0.38{col 46}{space 3}0.706{col 54}{space 4}-.7154496{col 67}{space 3} 1.054216
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .0775677{col 26}{space 2} .4531269{col 37}{space 1}    0.17{col 46}{space 3}0.864{col 54}{space 4}-.8155026{col 67}{space 3} .9706381
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .1168343{col 26}{space 2} .4477038{col 37}{space 1}    0.26{col 46}{space 3}0.794{col 54}{space 4}-.7655477{col 67}{space 3} .9992163
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .0961678{col 26}{space 2} .4509276{col 37}{space 1}    0.21{col 46}{space 3}0.831{col 54}{space 4}-.7925679{col 67}{space 3} .9849035
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .0572926{col 26}{space 2} .4464821{col 37}{space 1}    0.13{col 46}{space 3}0.898{col 54}{space 4}-.8226814{col 67}{space 3} .9372667
{txt}{space 9}46  {c |}{col 14}{res}{space 2}-.0696779{col 26}{space 2} .4423242{col 37}{space 1}   -0.16{col 46}{space 3}0.875{col 54}{space 4}-.9414571{col 67}{space 3} .8021012
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .1555037{col 26}{space 2} .4582587{col 37}{space 1}    0.34{col 46}{space 3}0.735{col 54}{space 4} -.747681{col 67}{space 3} 1.058688
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .1416201{col 26}{space 2} .4581201{col 37}{space 1}    0.31{col 46}{space 3}0.758{col 54}{space 4}-.7612912{col 67}{space 3} 1.044532
{txt}{space 9}49  {c |}{col 14}{res}{space 2}  .046385{col 26}{space 2} .4535871{col 37}{space 1}    0.10{col 46}{space 3}0.919{col 54}{space 4}-.8475923{col 67}{space 3} .9403624
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .0818359{col 26}{space 2} .4743803{col 37}{space 1}    0.17{col 46}{space 3}0.863{col 54}{space 4} -.853123{col 67}{space 3} 1.016795
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .1365318{col 26}{space 2} .4636627{col 37}{space 1}    0.29{col 46}{space 3}0.769{col 54}{space 4}-.7773036{col 67}{space 3} 1.050367
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .0754463{col 26}{space 2} .4615978{col 37}{space 1}    0.16{col 46}{space 3}0.870{col 54}{space 4}-.8343195{col 67}{space 3}  .985212
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .1645887{col 26}{space 2} .4660919{col 37}{space 1}    0.35{col 46}{space 3}0.724{col 54}{space 4}-.7540346{col 67}{space 3} 1.083212
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .1611217{col 26}{space 2} .4620713{col 37}{space 1}    0.35{col 46}{space 3}0.728{col 54}{space 4}-.7495772{col 67}{space 3} 1.071821
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .1779896{col 26}{space 2} .4595261{col 37}{space 1}    0.39{col 46}{space 3}0.699{col 54}{space 4}-.7276929{col 67}{space 3} 1.083672
{txt}{space 9}56  {c |}{col 14}{res}{space 2}-.0203543{col 26}{space 2} .4966496{col 37}{space 1}   -0.04{col 46}{space 3}0.967{col 54}{space 4}-.9992038{col 67}{space 3} .9584952
{txt}{space 9}57  {c |}{col 14}{res}{space 2}-.0488115{col 26}{space 2} .4914046{col 37}{space 1}   -0.10{col 46}{space 3}0.921{col 54}{space 4}-1.017323{col 67}{space 3} .9197006
{txt}{space 9}58  {c |}{col 14}{res}{space 2} -.008908{col 26}{space 2} .4491143{col 37}{space 1}   -0.02{col 46}{space 3}0.984{col 54}{space 4}  -.89407{col 67}{space 3}  .876254
{txt}{space 9}59  {c |}{col 14}{res}{space 2}  .084426{col 26}{space 2} .4778823{col 37}{space 1}    0.18{col 46}{space 3}0.860{col 54}{space 4} -.857435{col 67}{space 3} 1.026287
{txt}{space 9}60  {c |}{col 14}{res}{space 2}  .011103{col 26}{space 2} .4700679{col 37}{space 1}    0.02{col 46}{space 3}0.981{col 54}{space 4}-.9153565{col 67}{space 3} .9375625
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .0715995{col 26}{space 2} .4162133{col 37}{space 1}    0.17{col 46}{space 3}0.864{col 54}{space 4}-.7487177{col 67}{space 3} .8919166
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .0552943{col 26}{space 2} .4650408{col 37}{space 1}    0.12{col 46}{space 3}0.905{col 54}{space 4}-.8612572{col 67}{space 3} .9718458
{txt}{space 9}63  {c |}{col 14}{res}{space 2}-.0135485{col 26}{space 2} .4631458{col 37}{space 1}   -0.03{col 46}{space 3}0.977{col 54}{space 4}-.9263652{col 67}{space 3} .8992682
{txt}{space 9}64  {c |}{col 14}{res}{space 2}-.0300474{col 26}{space 2} .4581529{col 37}{space 1}   -0.07{col 46}{space 3}0.948{col 54}{space 4}-.9330235{col 67}{space 3} .8729287
{txt}{space 9}65  {c |}{col 14}{res}{space 2}-.1523549{col 26}{space 2}  .457185{col 37}{space 1}   -0.33{col 46}{space 3}0.739{col 54}{space 4}-1.053423{col 67}{space 3} .7487135
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .1775677{col 26}{space 2} .4532742{col 37}{space 1}    0.39{col 46}{space 3}0.696{col 54}{space 4} -.715793{col 67}{space 3} 1.070928
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .1143393{col 26}{space 2} .4546703{col 37}{space 1}    0.25{col 46}{space 3}0.802{col 54}{space 4}-.7817729{col 67}{space 3} 1.010452
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .0755521{col 26}{space 2} .4550896{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4}-.8213865{col 67}{space 3} .9724907
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .1761903{col 26}{space 2} .4552506{col 37}{space 1}    0.39{col 46}{space 3}0.699{col 54}{space 4}-.7210657{col 67}{space 3} 1.073446
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .0828236{col 26}{space 2} .4596811{col 37}{space 1}    0.18{col 46}{space 3}0.857{col 54}{space 4}-.8231644{col 67}{space 3} .9888117
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .4425603{col 26}{space 2} .4651744{col 37}{space 1}    0.95{col 46}{space 3}0.342{col 54}{space 4}-.4742546{col 67}{space 3} 1.359375
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reglong_alt.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reglong_alt.tex"'})

{com}. 
. 
. restore
{txt}
{com}. 
. ////////////////////////////////////
> ///TABLE A6 - LOGS/SINGLE CONTRY CODES
> /// this table combines three separate tables below
> 
. *SINGLE COUNTRIES
. gen singlecountries = 1 if IFAUkod==30 | IFAUkod==46 | IFAUkod==47 | IFAUkod==48
{txt}(35,637 missing values generated)

{com}. 
. preserve 
{txt}
{com}. keep if singlecountries==1
{txt}(35,637 observations deleted)

{com}. 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    26,820
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       758
{txt}{col 49}F({res}  27{txt},{res}    272{txt}){col 67}= {res}     92.37
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1152
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0885
{txt}{col 49}Root MSE{col 67}= {res}    0.4764

{txt}{ralign 78:(Std. Err. adjusted for {res:273} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0109756{col 26}{space 2} .0102575{col 37}{space 1}    1.07{col 46}{space 3}0.286{col 54}{space 4}-.0092186{col 67}{space 3} .0311698
{txt}{space 9}sex {c |}{col 14}{res}{space 2}  .034497{col 26}{space 2} .0077533{col 37}{space 1}    4.45{col 46}{space 3}0.000{col 54}{space 4} .0192328{col 67}{space 3} .0497611
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0151609{col 26}{space 2} .0012679{col 37}{space 1}   11.96{col 46}{space 3}0.000{col 54}{space 4} .0126647{col 67}{space 3} .0176571
{txt}{space 8}age2 {c |}{col 14}{res}{space 2} -.000192{col 26}{space 2} .0000144{col 37}{space 1}  -13.36{col 46}{space 3}0.000{col 54}{space 4}-.0002203{col 67}{space 3}-.0001637
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0267388{col 26}{space 2} .0098778{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .0072922{col 67}{space 3} .0461854
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0667892{col 26}{space 2} .0150936{col 37}{space 1}    4.43{col 46}{space 3}0.000{col 54}{space 4} .0370741{col 67}{space 3} .0965044
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0129884{col 26}{space 2} .0069375{col 37}{space 1}    1.87{col 46}{space 3}0.062{col 54}{space 4}-.0006695{col 67}{space 3} .0266464
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0838164{col 26}{space 2} .0074003{col 37}{space 1}   11.33{col 46}{space 3}0.000{col 54}{space 4} .0692472{col 67}{space 3} .0983856
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .0392134{col 26}{space 2} .0424645{col 37}{space 1}    0.92{col 46}{space 3}0.357{col 54}{space 4}-.0443876{col 67}{space 3} .1228144
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0227486{col 26}{space 2} .0423035{col 37}{space 1}   -0.54{col 46}{space 3}0.591{col 54}{space 4}-.1060325{col 67}{space 3} .0605353
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0560137{col 26}{space 2} .0383268{col 37}{space 1}   -1.46{col 46}{space 3}0.145{col 54}{space 4}-.1314686{col 67}{space 3} .0194412
{txt}{space 9}10  {c |}{col 14}{res}{space 2} -.034746{col 26}{space 2} .0487112{col 37}{space 1}   -0.71{col 46}{space 3}0.476{col 54}{space 4}-.1306449{col 67}{space 3} .0611528
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .1965704{col 26}{space 2} .0370945{col 37}{space 1}    5.30{col 46}{space 3}0.000{col 54}{space 4} .1235416{col 67}{space 3} .2695992
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .2127745{col 26}{space 2} .0382049{col 37}{space 1}    5.57{col 46}{space 3}0.000{col 54}{space 4} .1375596{col 67}{space 3} .2879893
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .1482044{col 26}{space 2} .0426904{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4} .0641589{col 67}{space 3}   .23225
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .1086754{col 26}{space 2} .0406467{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4} .0286533{col 67}{space 3} .1886975
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .1119001{col 26}{space 2} .0422911{col 37}{space 1}    2.65{col 46}{space 3}0.009{col 54}{space 4} .0286405{col 67}{space 3} .1951596
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .2009648{col 26}{space 2} .0506892{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4} .1011717{col 67}{space 3} .3007578
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .2017181{col 26}{space 2} .0514517{col 37}{space 1}    3.92{col 46}{space 3}0.000{col 54}{space 4} .1004239{col 67}{space 3} .3030123
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .2097173{col 26}{space 2} .0467462{col 37}{space 1}    4.49{col 46}{space 3}0.000{col 54}{space 4}  .117687{col 67}{space 3} .3017476
{txt}{space 9}49  {c |}{col 14}{res}{space 2}  .124459{col 26}{space 2} .0393205{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4} .0470479{col 67}{space 3} .2018701
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .1207852{col 26}{space 2} .0474579{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} .0273537{col 67}{space 3} .2142166
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .2873152{col 26}{space 2}   .05015{col 37}{space 1}    5.73{col 46}{space 3}0.000{col 54}{space 4} .1885837{col 67}{space 3} .3860467
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .2210089{col 26}{space 2} .0590724{col 37}{space 1}    3.74{col 46}{space 3}0.000{col 54}{space 4} .1047116{col 67}{space 3} .3373063
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .1724185{col 26}{space 2}  .045723{col 37}{space 1}    3.77{col 46}{space 3}0.000{col 54}{space 4} .0824025{col 67}{space 3} .2624344
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .1470448{col 26}{space 2} .0522938{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4} .0440928{col 67}{space 3} .2499968
{txt}{space 9}55  {c |}{col 14}{res}{space 2}  .163192{col 26}{space 2} .0504738{col 37}{space 1}    3.23{col 46}{space 3}0.001{col 54}{space 4} .0638231{col 67}{space 3}  .262561
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}  .020282{col 26}{space 2} .0475273{col 37}{space 1}    0.43{col 46}{space 3}0.670{col 54}{space 4}-.0732861{col 67}{space 3}   .11385
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     8,310
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       538
{txt}{col 49}F({res}  27{txt},{res}    243{txt}){col 67}= {res}     19.92
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1365
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0736
{txt}{col 49}Root MSE{col 67}= {res}    0.4742

{txt}{ralign 78:(Std. Err. adjusted for {res:244} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0336764{col 26}{space 2} .0176397{col 37}{space 1}   -1.91{col 46}{space 3}0.057{col 54}{space 4}-.0684226{col 67}{space 3} .0010699
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0403164{col 26}{space 2} .0119112{col 37}{space 1}    3.38{col 46}{space 3}0.001{col 54}{space 4} .0168542{col 67}{space 3} .0637787
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0204594{col 26}{space 2} .0029371{col 37}{space 1}    6.97{col 46}{space 3}0.000{col 54}{space 4}  .014674{col 67}{space 3} .0262447
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}  -.00027{col 26}{space 2} .0000346{col 37}{space 1}   -7.80{col 46}{space 3}0.000{col 54}{space 4}-.0003382{col 67}{space 3}-.0002017
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0452415{col 26}{space 2} .0188333{col 37}{space 1}    2.40{col 46}{space 3}0.017{col 54}{space 4} .0081441{col 67}{space 3} .0823388
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0534301{col 26}{space 2} .0233964{col 37}{space 1}    2.28{col 46}{space 3}0.023{col 54}{space 4} .0073445{col 67}{space 3} .0995157
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0128091{col 26}{space 2} .0088683{col 37}{space 1}    1.44{col 46}{space 3}0.150{col 54}{space 4}-.0046596{col 67}{space 3} .0302777
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0700789{col 26}{space 2} .0136138{col 37}{space 1}    5.15{col 46}{space 3}0.000{col 54}{space 4} .0432628{col 67}{space 3}  .096895
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .6414337{col 26}{space 2} .1471888{col 37}{space 1}    4.36{col 46}{space 3}0.000{col 54}{space 4}  .351505{col 67}{space 3} .9313625
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2457141{col 26}{space 2} .1208223{col 37}{space 1}    2.03{col 46}{space 3}0.043{col 54}{space 4} .0077215{col 67}{space 3} .4837067
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2329696{col 26}{space 2} .1437073{col 37}{space 1}    1.62{col 46}{space 3}0.106{col 54}{space 4}-.0501013{col 67}{space 3} .5160406
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1317627{col 26}{space 2} .1116804{col 37}{space 1}    1.18{col 46}{space 3}0.239{col 54}{space 4}-.0882225{col 67}{space 3} .3517478
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .5076202{col 26}{space 2} .0761952{col 37}{space 1}    6.66{col 46}{space 3}0.000{col 54}{space 4} .3575328{col 67}{space 3} .6577077
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .5419117{col 26}{space 2} .0744375{col 37}{space 1}    7.28{col 46}{space 3}0.000{col 54}{space 4} .3952866{col 67}{space 3} .6885367
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .4384707{col 26}{space 2} .0755052{col 37}{space 1}    5.81{col 46}{space 3}0.000{col 54}{space 4} .2897426{col 67}{space 3} .5871989
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .4231476{col 26}{space 2} .0746682{col 37}{space 1}    5.67{col 46}{space 3}0.000{col 54}{space 4}  .276068{col 67}{space 3} .5702271
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .4389211{col 26}{space 2} .0782363{col 37}{space 1}    5.61{col 46}{space 3}0.000{col 54}{space 4} .2848132{col 67}{space 3} .5930291
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .5017618{col 26}{space 2} .0891762{col 37}{space 1}    5.63{col 46}{space 3}0.000{col 54}{space 4} .3261049{col 67}{space 3} .6774187
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .4605495{col 26}{space 2} .0766365{col 37}{space 1}    6.01{col 46}{space 3}0.000{col 54}{space 4} .3095929{col 67}{space 3} .6115061
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .5036497{col 26}{space 2} .0802335{col 37}{space 1}    6.28{col 46}{space 3}0.000{col 54}{space 4} .3456078{col 67}{space 3} .6616917
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .4160276{col 26}{space 2} .0737175{col 37}{space 1}    5.64{col 46}{space 3}0.000{col 54}{space 4} .2708207{col 67}{space 3} .5612344
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .3902648{col 26}{space 2} .0737411{col 37}{space 1}    5.29{col 46}{space 3}0.000{col 54}{space 4} .2450114{col 67}{space 3} .5355181
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .6658864{col 26}{space 2} .1058056{col 37}{space 1}    6.29{col 46}{space 3}0.000{col 54}{space 4} .4574733{col 67}{space 3} .8742995
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5317737{col 26}{space 2}  .126612{col 37}{space 1}    4.20{col 46}{space 3}0.000{col 54}{space 4} .2823766{col 67}{space 3} .7811708
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5328454{col 26}{space 2} .1132764{col 37}{space 1}    4.70{col 46}{space 3}0.000{col 54}{space 4} .3097164{col 67}{space 3} .7559743
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .4369841{col 26}{space 2} .0923869{col 37}{space 1}    4.73{col 46}{space 3}0.000{col 54}{space 4} .2550028{col 67}{space 3} .6189654
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .4149816{col 26}{space 2}  .091957{col 37}{space 1}    4.51{col 46}{space 3}0.000{col 54}{space 4} .2338471{col 67}{space 3}  .596116
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3517076{col 26}{space 2} .0779134{col 37}{space 1}   -4.51{col 46}{space 3}0.000{col 54}{space 4}-.5051795{col 67}{space 3}-.1982357
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     8,916
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       535
{txt}{col 49}{help j_robustsingular##|_new:F(  25,    240)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1352
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0772
{txt}{col 49}Root MSE{col 67}= {res}    0.4780

{txt}{ralign 78:(Std. Err. adjusted for {res:241} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0402701{col 26}{space 2}  .016377{col 37}{space 1}   -2.46{col 46}{space 3}0.015{col 54}{space 4}-.0725311{col 67}{space 3} -.008009
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0482426{col 26}{space 2} .0113954{col 37}{space 1}    4.23{col 46}{space 3}0.000{col 54}{space 4} .0257948{col 67}{space 3} .0706905
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0132667{col 26}{space 2} .0027947{col 37}{space 1}    4.75{col 46}{space 3}0.000{col 54}{space 4} .0077614{col 67}{space 3}  .018772
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001875{col 26}{space 2} .0000304{col 37}{space 1}   -6.16{col 46}{space 3}0.000{col 54}{space 4}-.0002475{col 67}{space 3}-.0001276
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0716191{col 26}{space 2} .0180425{col 37}{space 1}    3.97{col 46}{space 3}0.000{col 54}{space 4} .0360773{col 67}{space 3}  .107161
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0479693{col 26}{space 2} .0253579{col 37}{space 1}    1.89{col 46}{space 3}0.060{col 54}{space 4}-.0019832{col 67}{space 3} .0979218
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0140768{col 26}{space 2} .0129351{col 37}{space 1}    1.09{col 46}{space 3}0.278{col 54}{space 4}-.0114041{col 67}{space 3} .0395577
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0892979{col 26}{space 2} .0120167{col 37}{space 1}    7.43{col 46}{space 3}0.000{col 54}{space 4} .0656262{col 67}{space 3} .1129696
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2} .6534963{col 26}{space 2} .2279295{col 37}{space 1}    2.87{col 46}{space 3}0.005{col 54}{space 4} .2044986{col 67}{space 3} 1.102494
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0749629{col 26}{space 2}  .054484{col 37}{space 1}    1.38{col 46}{space 3}0.170{col 54}{space 4}-.0323651{col 67}{space 3} .1822909
{txt}{space 9}10  {c |}{col 14}{res}{space 2}  .081375{col 26}{space 2}  .124149{col 37}{space 1}    0.66{col 46}{space 3}0.513{col 54}{space 4}-.1631858{col 67}{space 3} .3259358
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .4359032{col 26}{space 2} .1189637{col 37}{space 1}    3.66{col 46}{space 3}0.000{col 54}{space 4}  .201557{col 67}{space 3} .6702494
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .4758214{col 26}{space 2} .1166797{col 37}{space 1}    4.08{col 46}{space 3}0.000{col 54}{space 4} .2459744{col 67}{space 3} .7056683
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .4287045{col 26}{space 2} .1181855{col 37}{space 1}    3.63{col 46}{space 3}0.000{col 54}{space 4} .1958912{col 67}{space 3} .6615178
{txt}{space 9}44  {c |}{col 14}{res}{space 2}  .407878{col 26}{space 2} .1180733{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .1752858{col 67}{space 3} .6404703
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .3603192{col 26}{space 2}  .121791{col 37}{space 1}    2.96{col 46}{space 3}0.003{col 54}{space 4} .1204034{col 67}{space 3} .6002349
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .3486344{col 26}{space 2} .1432657{col 37}{space 1}    2.43{col 46}{space 3}0.016{col 54}{space 4} .0664157{col 67}{space 3} .6308531
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .3994474{col 26}{space 2} .1305387{col 37}{space 1}    3.06{col 46}{space 3}0.002{col 54}{space 4} .1422995{col 67}{space 3} .6565952
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .4614606{col 26}{space 2} .1210601{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .2229847{col 67}{space 3} .6999366
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .3471506{col 26}{space 2} .1196951{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4} .1113634{col 67}{space 3} .5829377
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .3460983{col 26}{space 2} .1229032{col 37}{space 1}    2.82{col 46}{space 3}0.005{col 54}{space 4} .1039916{col 67}{space 3} .5882049
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .4488214{col 26}{space 2}  .200027{col 37}{space 1}    2.24{col 46}{space 3}0.026{col 54}{space 4} .0547887{col 67}{space 3}  .842854
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .4058458{col 26}{space 2} .1487163{col 37}{space 1}    2.73{col 46}{space 3}0.007{col 54}{space 4} .1128898{col 67}{space 3} .6988018
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .4973406{col 26}{space 2} .1603801{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4} .1814082{col 67}{space 3} .8132729
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .4170827{col 26}{space 2} .1338257{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .1534597{col 67}{space 3} .6807057
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .3595952{col 26}{space 2} .1324379{col 37}{space 1}    2.72{col 46}{space 3}0.007{col 54}{space 4} .0987061{col 67}{space 3} .6204843
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2069354{col 26}{space 2} .1196848{col 37}{space 1}   -1.73{col 46}{space 3}0.085{col 54}{space 4}-.4427022{col 67}{space 3} .0288314
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     4,652
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       215
{txt}{col 49}F({res}  27{txt},{res}    105{txt}){col 67}= {res}    112.60
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1340
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0866
{txt}{col 49}Root MSE{col 67}= {res}    0.4778

{txt}{ralign 78:(Std. Err. adjusted for {res:106} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0248029{col 26}{space 2} .0276788{col 37}{space 1}   -0.90{col 46}{space 3}0.372{col 54}{space 4}-.0796848{col 67}{space 3} .0300791
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0196512{col 26}{space 2} .0111586{col 37}{space 1}    1.76{col 46}{space 3}0.081{col 54}{space 4}-.0024744{col 67}{space 3} .0417767
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0186231{col 26}{space 2} .0023429{col 37}{space 1}    7.95{col 46}{space 3}0.000{col 54}{space 4} .0139775{col 67}{space 3} .0232686
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002163{col 26}{space 2} .0000277{col 37}{space 1}   -7.81{col 46}{space 3}0.000{col 54}{space 4}-.0002711{col 67}{space 3}-.0001614
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0473583{col 26}{space 2} .0182541{col 37}{space 1}   -2.59{col 46}{space 3}0.011{col 54}{space 4}-.0835527{col 67}{space 3}-.0111638
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0552777{col 26}{space 2} .0498305{col 37}{space 1}    1.11{col 46}{space 3}0.270{col 54}{space 4}-.0435269{col 67}{space 3} .1540823
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0183695{col 26}{space 2} .0170162{col 37}{space 1}    1.08{col 46}{space 3}0.283{col 54}{space 4}-.0153705{col 67}{space 3} .0521096
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0913844{col 26}{space 2} .0255511{col 37}{space 1}    3.58{col 46}{space 3}0.001{col 54}{space 4} .0407213{col 67}{space 3} .1420475
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2}   .12133{col 26}{space 2} .1409469{col 37}{space 1}    0.86{col 46}{space 3}0.391{col 54}{space 4}-.1581416{col 67}{space 3} .4008017
{txt}{space 10}8  {c |}{col 14}{res}{space 2} -.087876{col 26}{space 2} .1403578{col 37}{space 1}   -0.63{col 46}{space 3}0.533{col 54}{space 4}-.3661795{col 67}{space 3} .1904275
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0146916{col 26}{space 2} .0742199{col 37}{space 1}   -0.20{col 46}{space 3}0.843{col 54}{space 4}-.1618559{col 67}{space 3} .1324727
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0559687{col 26}{space 2} .2240408{col 37}{space 1}    0.25{col 46}{space 3}0.803{col 54}{space 4}-.3882629{col 67}{space 3} .5002003
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .2633775{col 26}{space 2} .0465437{col 37}{space 1}    5.66{col 46}{space 3}0.000{col 54}{space 4} .1710898{col 67}{space 3} .3556651
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .2528098{col 26}{space 2} .0440955{col 37}{space 1}    5.73{col 46}{space 3}0.000{col 54}{space 4} .1653765{col 67}{space 3}  .340243
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .2525357{col 26}{space 2}  .047597{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4} .1581597{col 67}{space 3} .3469117
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .1433132{col 26}{space 2} .0548893{col 37}{space 1}    2.61{col 46}{space 3}0.010{col 54}{space 4} .0344778{col 67}{space 3} .2521485
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .1651873{col 26}{space 2} .0671193{col 37}{space 1}    2.46{col 46}{space 3}0.015{col 54}{space 4} .0321022{col 67}{space 3} .2982724
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .4066429{col 26}{space 2} .0846196{col 37}{space 1}    4.81{col 46}{space 3}0.000{col 54}{space 4} .2388579{col 67}{space 3}  .574428
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .3397441{col 26}{space 2} .0660639{col 37}{space 1}    5.14{col 46}{space 3}0.000{col 54}{space 4} .2087515{col 67}{space 3} .4707367
{txt}{space 9}48  {c |}{col 14}{res}{space 2}   .24887{col 26}{space 2} .0549897{col 37}{space 1}    4.53{col 46}{space 3}0.000{col 54}{space 4} .1398355{col 67}{space 3} .3579045
{txt}{space 9}49  {c |}{col 14}{res}{space 2}  .156153{col 26}{space 2} .0703047{col 37}{space 1}    2.22{col 46}{space 3}0.028{col 54}{space 4} .0167517{col 67}{space 3} .2955542
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .2708486{col 26}{space 2} .0844416{col 37}{space 1}    3.21{col 46}{space 3}0.002{col 54}{space 4} .1034164{col 67}{space 3} .4382808
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .4090584{col 26}{space 2} .0571754{col 37}{space 1}    7.15{col 46}{space 3}0.000{col 54}{space 4} .2956901{col 67}{space 3} .5224267
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .3202054{col 26}{space 2} .0774477{col 37}{space 1}    4.13{col 46}{space 3}0.000{col 54}{space 4}  .166641{col 67}{space 3} .4737699
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .2799412{col 26}{space 2} .0596811{col 37}{space 1}    4.69{col 46}{space 3}0.000{col 54}{space 4} .1616047{col 67}{space 3} .3982777
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .2831893{col 26}{space 2} .0767695{col 37}{space 1}    3.69{col 46}{space 3}0.000{col 54}{space 4} .1309695{col 67}{space 3}  .435409
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .3881999{col 26}{space 2} .0740243{col 37}{space 1}    5.24{col 46}{space 3}0.000{col 54}{space 4} .2414234{col 67}{space 3} .5349764
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1229249{col 26}{space 2} .0689459{col 37}{space 1}   -1.78{col 46}{space 3}0.077{col 54}{space 4}-.2596318{col 67}{space 3}  .013782
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     4,942
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       507
{txt}{col 49}F({res}  27{txt},{res}    214{txt}){col 67}= {res}     18.52
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2288
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1355
{txt}{col 49}Root MSE{col 67}= {res}    0.4606

{txt}{ralign 78:(Std. Err. adjusted for {res:215} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0378519{col 26}{space 2} .0088688{col 37}{space 1}    4.27{col 46}{space 3}0.000{col 54}{space 4} .0203705{col 67}{space 3} .0553334
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0072048{col 26}{space 2} .0108824{col 37}{space 1}    0.66{col 46}{space 3}0.509{col 54}{space 4}-.0142456{col 67}{space 3} .0286552
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0137283{col 26}{space 2} .0028132{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .0081831{col 67}{space 3} .0192735
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001656{col 26}{space 2} .0000309{col 37}{space 1}   -5.37{col 46}{space 3}0.000{col 54}{space 4}-.0002265{col 67}{space 3}-.0001048
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0064231{col 26}{space 2} .0180684{col 37}{space 1}    0.36{col 46}{space 3}0.723{col 54}{space 4}-.0291918{col 67}{space 3} .0420379
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .1378214{col 26}{space 2}  .034231{col 37}{space 1}    4.03{col 46}{space 3}0.000{col 54}{space 4} .0703483{col 67}{space 3} .2052946
{txt}{space 4}children {c |}{col 14}{res}{space 2}-.0016602{col 26}{space 2} .0145807{col 37}{space 1}   -0.11{col 46}{space 3}0.909{col 54}{space 4}-.0304004{col 67}{space 3}   .02708
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0737064{col 26}{space 2} .0178697{col 37}{space 1}    4.12{col 46}{space 3}0.000{col 54}{space 4} .0384831{col 67}{space 3} .1089296
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .0261718{col 26}{space 2} .0510319{col 37}{space 1}    0.51{col 46}{space 3}0.609{col 54}{space 4}-.0744178{col 67}{space 3} .1267614
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0455583{col 26}{space 2}  .044055{col 37}{space 1}   -1.03{col 46}{space 3}0.302{col 54}{space 4}-.1323956{col 67}{space 3} .0412789
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0704558{col 26}{space 2} .0398749{col 37}{space 1}   -1.77{col 46}{space 3}0.079{col 54}{space 4}-.1490537{col 67}{space 3}  .008142
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0538255{col 26}{space 2}  .052989{col 37}{space 1}   -1.02{col 46}{space 3}0.311{col 54}{space 4}-.1582727{col 67}{space 3} .0506217
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .0122307{col 26}{space 2}  .078295{col 37}{space 1}    0.16{col 46}{space 3}0.876{col 54}{space 4}-.1420975{col 67}{space 3} .1665588
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .0158194{col 26}{space 2} .0865828{col 37}{space 1}    0.18{col 46}{space 3}0.855{col 54}{space 4} -.154845{col 67}{space 3} .1864837
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .2866788{col 26}{space 2} .1679801{col 37}{space 1}    1.71{col 46}{space 3}0.089{col 54}{space 4}-.0444286{col 67}{space 3} .6177861
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .2581577{col 26}{space 2} .2057279{col 37}{space 1}    1.25{col 46}{space 3}0.211{col 54}{space 4}-.1473548{col 67}{space 3} .6636703
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .2632588{col 26}{space 2} .2292258{col 37}{space 1}    1.15{col 46}{space 3}0.252{col 54}{space 4}-.1885707{col 67}{space 3} .7150884
{txt}{space 9}46  {c |}{col 14}{res}{space 2}-.0119759{col 26}{space 2} .1183068{col 37}{space 1}   -0.10{col 46}{space 3}0.919{col 54}{space 4}-.2451717{col 67}{space 3}   .22122
{txt}{space 9}47  {c |}{col 14}{res}{space 2}  .199936{col 26}{space 2} .1075314{col 37}{space 1}    1.86{col 46}{space 3}0.064{col 54}{space 4}-.0120204{col 67}{space 3} .4118925
{txt}{space 9}48  {c |}{col 14}{res}{space 2}  .152774{col 26}{space 2} .1231978{col 37}{space 1}    1.24{col 46}{space 3}0.216{col 54}{space 4}-.0900625{col 67}{space 3} .3956105
{txt}{space 9}49  {c |}{col 14}{res}{space 2}   .24703{col 26}{space 2} .1236833{col 37}{space 1}    2.00{col 46}{space 3}0.047{col 54}{space 4} .0032365{col 67}{space 3} .4908234
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .2166831{col 26}{space 2} .1305058{col 37}{space 1}    1.66{col 46}{space 3}0.098{col 54}{space 4}-.0405583{col 67}{space 3} .4739245
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .2517703{col 26}{space 2} .0580021{col 37}{space 1}    4.34{col 46}{space 3}0.000{col 54}{space 4} .1374417{col 67}{space 3} .3660989
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .2078498{col 26}{space 2} .0698157{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .0702352{col 67}{space 3} .3454644
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .1532941{col 26}{space 2} .0531871{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4} .0484564{col 67}{space 3} .2581319
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .1258139{col 26}{space 2} .0669122{col 37}{space 1}    1.88{col 46}{space 3}0.061{col 54}{space 4}-.0060776{col 67}{space 3} .2577054
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .1423849{col 26}{space 2} .0582746{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4} .0275193{col 67}{space 3} .2572506
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0335346{col 26}{space 2} .0799569{col 37}{space 1}    0.42{col 46}{space 3}0.675{col 54}{space 4}-.1240694{col 67}{space 3} .1911386
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_singlecountries.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_singlecountries.tex"'})

{com}. 
. restore 
{txt}
{com}. 
. ///FOCUS ON LOGCOETHNICS - FUNCTIONAL FORM
> 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    60,737
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       970
{txt}{col 49}F({res}  77{txt},{res}    284{txt}){col 67}= {res}    129.26
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1077
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0921
{txt}{col 49}Root MSE{col 67}= {res}    0.4762

{txt}{ralign 78:(Std. Err. adjusted for {res:285} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2} .0057774{col 26}{space 2}  .004039{col 37}{space 1}    1.43{col 46}{space 3}0.154{col 54}{space 4}-.0021728{col 67}{space 3} .0137276
{txt}{space 9}sex {c |}{col 14}{res}{space 2}  .018191{col 26}{space 2} .0046317{col 37}{space 1}    3.93{col 46}{space 3}0.000{col 54}{space 4} .0090741{col 67}{space 3} .0273078
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0195991{col 26}{space 2} .0012935{col 37}{space 1}   15.15{col 46}{space 3}0.000{col 54}{space 4}  .017053{col 67}{space 3} .0221452
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002303{col 26}{space 2}  .000015{col 37}{space 1}  -15.31{col 46}{space 3}0.000{col 54}{space 4}-.0002599{col 67}{space 3}-.0002007
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0117897{col 26}{space 2} .0051847{col 37}{space 1}    2.27{col 46}{space 3}0.024{col 54}{space 4} .0015844{col 67}{space 3}  .021995
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0511961{col 26}{space 2} .0085941{col 37}{space 1}    5.96{col 46}{space 3}0.000{col 54}{space 4} .0342799{col 67}{space 3} .0681124
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0152854{col 26}{space 2}  .003574{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} .0082504{col 67}{space 3} .0223203
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0791795{col 26}{space 2} .0056134{col 37}{space 1}   14.11{col 46}{space 3}0.000{col 54}{space 4} .0681303{col 67}{space 3} .0902287
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .2279202{col 26}{space 2} .3535476{col 37}{space 1}    0.64{col 46}{space 3}0.520{col 54}{space 4}-.4679859{col 67}{space 3} .9238264
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0658358{col 26}{space 2} .2231442{col 37}{space 1}    0.30{col 46}{space 3}0.768{col 54}{space 4}-.3733905{col 67}{space 3} .5050622
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{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1162204{col 26}{space 2} .2558919{col 37}{space 1}    0.45{col 46}{space 3}0.650{col 54}{space 4} -.387465{col 67}{space 3} .6199057
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1737675{col 26}{space 2} .1654755{col 37}{space 1}    1.05{col 46}{space 3}0.295{col 54}{space 4}-.1519465{col 67}{space 3} .4994815
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2175645{col 26}{space 2} .1438616{col 37}{space 1}    1.51{col 46}{space 3}0.132{col 54}{space 4}-.0656059{col 67}{space 3} .5007348
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1534995{col 26}{space 2}  .153203{col 37}{space 1}    1.00{col 46}{space 3}0.317{col 54}{space 4}-.1480579{col 67}{space 3} .4550569
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1154969{col 26}{space 2} .1464034{col 37}{space 1}    0.79{col 46}{space 3}0.431{col 54}{space 4}-.1726766{col 67}{space 3} .4036704
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1241343{col 26}{space 2} .1545884{col 37}{space 1}    0.80{col 46}{space 3}0.423{col 54}{space 4}-.1801501{col 67}{space 3} .4284188
{txt}{space 9}11  {c |}{col 14}{res}{space 2}  .480513{col 26}{space 2} .1555144{col 37}{space 1}    3.09{col 46}{space 3}0.002{col 54}{space 4} .1744059{col 67}{space 3}   .78662
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .3983561{col 26}{space 2} .1522723{col 37}{space 1}    2.62{col 46}{space 3}0.009{col 54}{space 4} .0986307{col 67}{space 3} .6980816
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3666004{col 26}{space 2} .1372484{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4} .0964472{col 67}{space 3} .6367536
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{txt}{space 9}15  {c |}{col 14}{res}{space 2} .3743621{col 26}{space 2} .1539807{col 37}{space 1}    2.43{col 46}{space 3}0.016{col 54}{space 4} .0712738{col 67}{space 3} .6774504
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{txt}{space 9}17  {c |}{col 14}{res}{space 2} .4618443{col 26}{space 2}  .134618{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} .1968686{col 67}{space 3}   .72682
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{txt}{space 9}26  {c |}{col 14}{res}{space 2} .3978493{col 26}{space 2} .1335053{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .1350639{col 67}{space 3} .6606348
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{txt}{space 9}28  {c |}{col 14}{res}{space 2} .2910353{col 26}{space 2} .1303098{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0345397{col 67}{space 3} .5475309
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .2468414{col 26}{space 2} .1332138{col 37}{space 1}    1.85{col 46}{space 3}0.065{col 54}{space 4}-.0153703{col 67}{space 3} .5090532
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{txt}{space 9}37  {c |}{col 14}{res}{space 2} .3113614{col 26}{space 2} .1341876{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4}  .047233{col 67}{space 3} .5754898
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .4220333{col 26}{space 2} .1343112{col 37}{space 1}    3.14{col 46}{space 3}0.002{col 54}{space 4} .1576616{col 67}{space 3}  .686405
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{txt}{space 9}48  {c |}{col 14}{res}{space 2}  .380866{col 26}{space 2} .1328174{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .1194347{col 67}{space 3} .6422973
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .3073025{col 26}{space 2} .1374647{col 37}{space 1}    2.24{col 46}{space 3}0.026{col 54}{space 4} .0367235{col 67}{space 3} .5778815
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .2893141{col 26}{space 2}  .129765{col 37}{space 1}    2.23{col 46}{space 3}0.027{col 54}{space 4} .0338909{col 67}{space 3} .5447373
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .4639783{col 26}{space 2} .1449351{col 37}{space 1}    3.20{col 46}{space 3}0.002{col 54}{space 4} .1786949{col 67}{space 3} .7492618
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{txt}{space 9}64  {c |}{col 14}{res}{space 2} .1915408{col 26}{space 2} .1373378{col 37}{space 1}    1.39{col 46}{space 3}0.164{col 54}{space 4}-.0787884{col 67}{space 3} .4618699
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .1074973{col 26}{space 2} .1243714{col 37}{space 1}    0.86{col 46}{space 3}0.388{col 54}{space 4}-.1373094{col 67}{space 3} .3523041
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .4688422{col 26}{space 2} .1482098{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4}  .177113{col 67}{space 3} .7605713
{txt}{space 9}67  {c |}{col 14}{res}{space 2}  .333092{col 26}{space 2} .1385631{col 37}{space 1}    2.40{col 46}{space 3}0.017{col 54}{space 4}  .060351{col 67}{space 3}  .605833
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .4242956{col 26}{space 2} .1419739{col 37}{space 1}    2.99{col 46}{space 3}0.003{col 54}{space 4} .1448409{col 67}{space 3} .7037503
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .3374552{col 26}{space 2} .1439027{col 37}{space 1}    2.35{col 46}{space 3}0.020{col 54}{space 4} .0542041{col 67}{space 3} .6207064
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .3000796{col 26}{space 2} .1473647{col 37}{space 1}    2.04{col 46}{space 3}0.043{col 54}{space 4} .0100141{col 67}{space 3} .5901452
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2543327{col 26}{space 2} .1376731{col 37}{space 1}   -1.85{col 46}{space 3}0.066{col 54}{space 4} -.525322{col 67}{space 3} .0166565
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 6.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,332
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       825
{txt}{col 49}F({res}  73{txt},{res}    277{txt}){col 67}= {res}     19.79
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1528
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1001
{txt}{col 49}Root MSE{col 67}= {res}    0.4717

{txt}{ralign 78:(Std. Err. adjusted for {res:278} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2} .0023219{col 26}{space 2} .0063422{col 37}{space 1}    0.37{col 46}{space 3}0.715{col 54}{space 4}-.0101632{col 67}{space 3}  .014807
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0284587{col 26}{space 2}  .008714{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0113046{col 67}{space 3} .0456129
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0255888{col 26}{space 2} .0018034{col 37}{space 1}   14.19{col 46}{space 3}0.000{col 54}{space 4} .0220386{col 67}{space 3}  .029139
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0003098{col 26}{space 2} .0000219{col 37}{space 1}  -14.15{col 46}{space 3}0.000{col 54}{space 4}-.0003529{col 67}{space 3}-.0002667
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0264655{col 26}{space 2} .0122474{col 37}{space 1}    2.16{col 46}{space 3}0.032{col 54}{space 4} .0023556{col 67}{space 3} .0505753
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0414126{col 26}{space 2} .0164835{col 37}{space 1}    2.51{col 46}{space 3}0.013{col 54}{space 4} .0089638{col 67}{space 3} .0738614
{txt}{space 4}children {c |}{col 14}{res}{space 2}  .013536{col 26}{space 2} .0052681{col 37}{space 1}    2.57{col 46}{space 3}0.011{col 54}{space 4} .0031655{col 67}{space 3} .0239066
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2}  .056806{col 26}{space 2} .0106314{col 37}{space 1}    5.34{col 46}{space 3}0.000{col 54}{space 4} .0358774{col 67}{space 3} .0777346
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2} .0633361{col 26}{space 2} .3227387{col 37}{space 1}    0.20{col 46}{space 3}0.845{col 54}{space 4}-.5719961{col 67}{space 3} .6986683
{txt}{space 10}6  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}7  {c |}{col 14}{res}{space 2} .6003085{col 26}{space 2} .3068701{col 37}{space 1}    1.96{col 46}{space 3}0.051{col 54}{space 4}-.0037852{col 67}{space 3} 1.204402
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1770402{col 26}{space 2} .1809722{col 37}{space 1}    0.98{col 46}{space 3}0.329{col 54}{space 4}-.1792154{col 67}{space 3} .5332958
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3266132{col 26}{space 2}  .195335{col 37}{space 1}    1.67{col 46}{space 3}0.096{col 54}{space 4}-.0579163{col 67}{space 3} .7111428
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2838465{col 26}{space 2}  .179872{col 37}{space 1}    1.58{col 46}{space 3}0.116{col 54}{space 4}-.0702433{col 67}{space 3} .6379363
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .7633307{col 26}{space 2} .1725175{col 37}{space 1}    4.42{col 46}{space 3}0.000{col 54}{space 4} .4237188{col 67}{space 3} 1.102943
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .6291084{col 26}{space 2} .1663214{col 37}{space 1}    3.78{col 46}{space 3}0.000{col 54}{space 4} .3016939{col 67}{space 3}  .956523
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5987425{col 26}{space 2} .1683327{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .2673685{col 67}{space 3} .9301165
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5689165{col 26}{space 2}  .161056{col 37}{space 1}    3.53{col 46}{space 3}0.000{col 54}{space 4} .2518672{col 67}{space 3} .8859658
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .6521267{col 26}{space 2} .1630987{col 37}{space 1}    4.00{col 46}{space 3}0.000{col 54}{space 4} .3310562{col 67}{space 3} .9731971
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .7494295{col 26}{space 2} .1487259{col 37}{space 1}    5.04{col 46}{space 3}0.000{col 54}{space 4} .4566528{col 67}{space 3} 1.042206
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .6829123{col 26}{space 2} .1471236{col 37}{space 1}    4.64{col 46}{space 3}0.000{col 54}{space 4}   .39329{col 67}{space 3} .9725346
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .6326637{col 26}{space 2} .1467331{col 37}{space 1}    4.31{col 46}{space 3}0.000{col 54}{space 4} .3438101{col 67}{space 3} .9215173
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .6160987{col 26}{space 2} .1537345{col 37}{space 1}    4.01{col 46}{space 3}0.000{col 54}{space 4} .3134623{col 67}{space 3}  .918735
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .4109967{col 26}{space 2} .1760937{col 37}{space 1}    2.33{col 46}{space 3}0.020{col 54}{space 4} .0643448{col 67}{space 3} .7576486
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .5024665{col 26}{space 2} .2060335{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4} .0968762{col 67}{space 3} .9080568
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{txt}{space 9}24  {c |}{col 14}{res}{space 2} .4301778{col 26}{space 2} .1797693{col 37}{space 1}    2.39{col 46}{space 3}0.017{col 54}{space 4} .0762901{col 67}{space 3} .7840654
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5583364{col 26}{space 2} .1987742{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4} .1670365{col 67}{space 3} .9496362
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{txt}{space 9}28  {c |}{col 14}{res}{space 2} .5039617{col 26}{space 2}  .148009{col 37}{space 1}    3.40{col 46}{space 3}0.001{col 54}{space 4} .2125964{col 67}{space 3} .7953269
{txt}{space 9}29  {c |}{col 14}{res}{space 2}   .48798{col 26}{space 2} .1496996{col 37}{space 1}    3.26{col 46}{space 3}0.001{col 54}{space 4} .1932866{col 67}{space 3} .7826734
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .4068571{col 26}{space 2} .1461315{col 37}{space 1}    2.78{col 46}{space 3}0.006{col 54}{space 4} .1191877{col 67}{space 3} .6945265
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .6758126{col 26}{space 2} .1671634{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4} .3467407{col 67}{space 3} 1.004885
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{txt}{space 9}45  {c |}{col 14}{res}{space 2} .5505554{col 26}{space 2} .1485506{col 37}{space 1}    3.71{col 46}{space 3}0.000{col 54}{space 4} .2581238{col 67}{space 3} .8429869
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{txt}{space 9}49  {c |}{col 14}{res}{space 2} .5590369{col 26}{space 2} .1474254{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4} .2688205{col 67}{space 3} .8492534
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .5092468{col 26}{space 2} .1474302{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .2190208{col 67}{space 3} .7994728
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{txt}{space 9}58  {c |}{col 14}{res}{space 2} .3793616{col 26}{space 2} .1560999{col 37}{space 1}    2.43{col 46}{space 3}0.016{col 54}{space 4} .0720687{col 67}{space 3} .6866545
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .4088623{col 26}{space 2} .1775072{col 37}{space 1}    2.30{col 46}{space 3}0.022{col 54}{space 4}  .059428{col 67}{space 3} .7582967
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .3063929{col 26}{space 2}  .170188{col 37}{space 1}    1.80{col 46}{space 3}0.073{col 54}{space 4}-.0286332{col 67}{space 3}  .641419
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5030024{col 26}{space 2} .1794494{col 37}{space 1}    2.80{col 46}{space 3}0.005{col 54}{space 4} .1497447{col 67}{space 3} .8562601
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .5305728{col 26}{space 2} .1819951{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .1723037{col 67}{space 3}  .888842
{txt}{space 9}63  {c |}{col 14}{res}{space 2}  .328476{col 26}{space 2} .1533358{col 37}{space 1}    2.14{col 46}{space 3}0.033{col 54}{space 4} .0266246{col 67}{space 3} .6303275
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .4186961{col 26}{space 2} .1542813{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1149833{col 67}{space 3} .7224089
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .3818062{col 26}{space 2}  .149553{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} .0874014{col 67}{space 3}  .676211
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .7697596{col 26}{space 2} .1991777{col 37}{space 1}    3.86{col 46}{space 3}0.000{col 54}{space 4} .3776654{col 67}{space 3} 1.161854
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .5354523{col 26}{space 2} .1830643{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .1750784{col 67}{space 3} .8958262
{txt}{space 9}68  {c |}{col 14}{res}{space 2}  .730583{col 26}{space 2} .1652402{col 37}{space 1}    4.42{col 46}{space 3}0.000{col 54}{space 4} .4052968{col 67}{space 3} 1.055869
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6244702{col 26}{space 2} .1740948{col 37}{space 1}    3.59{col 46}{space 3}0.000{col 54}{space 4} .2817533{col 67}{space 3}  .967187
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .5579789{col 26}{space 2} .1647316{col 37}{space 1}    3.39{col 46}{space 3}0.001{col 54}{space 4}  .233694{col 67}{space 3} .8822638
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.5655639{col 26}{space 2} .1476111{col 37}{space 1}   -3.83{col 46}{space 3}0.000{col 54}{space 4}-.8561459{col 67}{space 3}-.2749819
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,261
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       754
{txt}{col 49}{help j_robustsingular##|_new:F(  73,    275)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1431
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0939
{txt}{col 49}Root MSE{col 67}= {res}    0.4753

{txt}{ralign 78:(Std. Err. adjusted for {res:276} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2}-.0017941{col 26}{space 2} .0076276{col 37}{space 1}   -0.24{col 46}{space 3}0.814{col 54}{space 4}  -.01681{col 67}{space 3} .0132218
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0337432{col 26}{space 2} .0105556{col 37}{space 1}    3.20{col 46}{space 3}0.002{col 54}{space 4} .0129632{col 67}{space 3} .0545231
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0208679{col 26}{space 2} .0027312{col 37}{space 1}    7.64{col 46}{space 3}0.000{col 54}{space 4} .0154911{col 67}{space 3} .0262446
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002568{col 26}{space 2} .0000291{col 37}{space 1}   -8.81{col 46}{space 3}0.000{col 54}{space 4}-.0003141{col 67}{space 3}-.0001994
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0306664{col 26}{space 2} .0131558{col 37}{space 1}    2.33{col 46}{space 3}0.020{col 54}{space 4} .0047676{col 67}{space 3} .0565652
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0466067{col 26}{space 2} .0171372{col 37}{space 1}    2.72{col 46}{space 3}0.007{col 54}{space 4}   .01287{col 67}{space 3} .0803435
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0134646{col 26}{space 2} .0073206{col 37}{space 1}    1.84{col 46}{space 3}0.067{col 54}{space 4}-.0009469{col 67}{space 3}  .027876
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0819719{col 26}{space 2} .0112222{col 37}{space 1}    7.30{col 46}{space 3}0.000{col 54}{space 4} .0598796{col 67}{space 3} .1040642
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.2760053{col 26}{space 2} .0789674{col 37}{space 1}   -3.50{col 46}{space 3}0.001{col 54}{space 4}-.4314627{col 67}{space 3}-.1205478
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0082528{col 26}{space 2} .0819442{col 37}{space 1}    0.10{col 46}{space 3}0.920{col 54}{space 4}-.1530647{col 67}{space 3} .1695703
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0889749{col 26}{space 2} .0750613{col 37}{space 1}   -1.19{col 46}{space 3}0.237{col 54}{space 4}-.2367427{col 67}{space 3} .0587929
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1507607{col 26}{space 2} .1106135{col 37}{space 1}    1.36{col 46}{space 3}0.174{col 54}{space 4}-.0669961{col 67}{space 3} .3685174
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2} .6598625{col 26}{space 2} .2290947{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4} .2088603{col 67}{space 3} 1.110865
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3191495{col 26}{space 2} .1531748{col 37}{space 1}    2.08{col 46}{space 3}0.038{col 54}{space 4} .0176052{col 67}{space 3} .6206937
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2061286{col 26}{space 2} .1440192{col 37}{space 1}    1.43{col 46}{space 3}0.153{col 54}{space 4}-.0773915{col 67}{space 3} .4896487
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .8313963{col 26}{space 2} .1379278{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4} .5598678{col 67}{space 3} 1.102925
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5583293{col 26}{space 2} .1059449{col 37}{space 1}    5.27{col 46}{space 3}0.000{col 54}{space 4} .3497632{col 67}{space 3} .7668954
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5564809{col 26}{space 2}  .128152{col 37}{space 1}    4.34{col 46}{space 3}0.000{col 54}{space 4} .3041973{col 67}{space 3} .8087645
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .6084279{col 26}{space 2} .0873636{col 37}{space 1}    6.96{col 46}{space 3}0.000{col 54}{space 4} .4364415{col 67}{space 3} .7804143
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .4822143{col 26}{space 2} .1502894{col 37}{space 1}    3.21{col 46}{space 3}0.001{col 54}{space 4} .1863504{col 67}{space 3} .7780783
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .6392779{col 26}{space 2} .0950768{col 37}{space 1}    6.72{col 46}{space 3}0.000{col 54}{space 4}  .452107{col 67}{space 3} .8264488
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .6184254{col 26}{space 2} .0932875{col 37}{space 1}    6.63{col 46}{space 3}0.000{col 54}{space 4}  .434777{col 67}{space 3} .8020738
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5880606{col 26}{space 2} .0946823{col 37}{space 1}    6.21{col 46}{space 3}0.000{col 54}{space 4} .4016665{col 67}{space 3} .7744548
{txt}{space 9}19  {c |}{col 14}{res}{space 2}  .553975{col 26}{space 2} .1044409{col 37}{space 1}    5.30{col 46}{space 3}0.000{col 54}{space 4} .3483697{col 67}{space 3} .7595804
{txt}{space 9}20  {c |}{col 14}{res}{space 2}  .678949{col 26}{space 2} .1591188{col 37}{space 1}    4.27{col 46}{space 3}0.000{col 54}{space 4} .3657033{col 67}{space 3} .9921947
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .7249195{col 26}{space 2} .2108204{col 37}{space 1}    3.44{col 46}{space 3}0.001{col 54}{space 4} .3098926{col 67}{space 3} 1.139946
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .7294713{col 26}{space 2} .2341124{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .2685911{col 67}{space 3} 1.190351
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .5625225{col 26}{space 2} .1542979{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .2587672{col 67}{space 3} .8662777
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .3542948{col 26}{space 2} .1347106{col 37}{space 1}    2.63{col 46}{space 3}0.009{col 54}{space 4} .0890998{col 67}{space 3} .6194898
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .3992221{col 26}{space 2} .1465241{col 37}{space 1}    2.72{col 46}{space 3}0.007{col 54}{space 4} .1107706{col 67}{space 3} .6876735
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .4925892{col 26}{space 2} .1011595{col 37}{space 1}    4.87{col 46}{space 3}0.000{col 54}{space 4} .2934437{col 67}{space 3} .6917346
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .5325524{col 26}{space 2}  .091911{col 37}{space 1}    5.79{col 46}{space 3}0.000{col 54}{space 4} .3516139{col 67}{space 3} .7134909
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .4457571{col 26}{space 2} .0914245{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .2657763{col 67}{space 3} .6257378
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .3847034{col 26}{space 2} .0975271{col 37}{space 1}    3.94{col 46}{space 3}0.000{col 54}{space 4} .1927088{col 67}{space 3}  .576698
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .3087751{col 26}{space 2} .1034485{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .1051236{col 67}{space 3} .5124267
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .5894719{col 26}{space 2} .1073325{col 37}{space 1}    5.49{col 46}{space 3}0.000{col 54}{space 4} .3781742{col 67}{space 3} .8007696
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .5318055{col 26}{space 2} .1122191{col 37}{space 1}    4.74{col 46}{space 3}0.000{col 54}{space 4} .3108878{col 67}{space 3} .7527232
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .4696719{col 26}{space 2} .0970836{col 37}{space 1}    4.84{col 46}{space 3}0.000{col 54}{space 4} .2785504{col 67}{space 3} .6607934
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .3410807{col 26}{space 2} .0961777{col 37}{space 1}    3.55{col 46}{space 3}0.000{col 54}{space 4} .1517426{col 67}{space 3} .5304189
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .4390912{col 26}{space 2} .1007441{col 37}{space 1}    4.36{col 46}{space 3}0.000{col 54}{space 4} .2407637{col 67}{space 3} .6374188
{txt}{space 9}36  {c |}{col 14}{res}{space 2}   .94932{col 26}{space 2} .1267391{col 37}{space 1}    7.49{col 46}{space 3}0.000{col 54}{space 4} .6998179{col 67}{space 3} 1.198822
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .4068794{col 26}{space 2}  .141192{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4}  .128925{col 67}{space 3} .6848338
{txt}{space 9}38  {c |}{col 14}{res}{space 2}   .41576{col 26}{space 2} .1246696{col 37}{space 1}    3.33{col 46}{space 3}0.001{col 54}{space 4} .1703319{col 67}{space 3}  .661188
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .5522424{col 26}{space 2} .1443998{col 37}{space 1}    3.82{col 46}{space 3}0.000{col 54}{space 4} .2679728{col 67}{space 3} .8365119
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5333238{col 26}{space 2} .1847362{col 37}{space 1}    2.89{col 46}{space 3}0.004{col 54}{space 4} .1696469{col 67}{space 3} .8970007
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .5261168{col 26}{space 2} .0917289{col 37}{space 1}    5.74{col 46}{space 3}0.000{col 54}{space 4} .3455367{col 67}{space 3} .7066968
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .5538508{col 26}{space 2} .0969952{col 37}{space 1}    5.71{col 46}{space 3}0.000{col 54}{space 4} .3629034{col 67}{space 3} .7447983
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .4981776{col 26}{space 2} .0951009{col 37}{space 1}    5.24{col 46}{space 3}0.000{col 54}{space 4} .3109593{col 67}{space 3} .6853959
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .4696004{col 26}{space 2} .0969748{col 37}{space 1}    4.84{col 46}{space 3}0.000{col 54}{space 4} .2786932{col 67}{space 3} .6605077
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .4240345{col 26}{space 2} .0868528{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .2530537{col 67}{space 3} .5950153
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .4702881{col 26}{space 2} .0930283{col 37}{space 1}    5.06{col 46}{space 3}0.000{col 54}{space 4} .2871501{col 67}{space 3} .6534262
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .4918987{col 26}{space 2} .0748522{col 37}{space 1}    6.57{col 46}{space 3}0.000{col 54}{space 4} .3445426{col 67}{space 3} .6392548
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .5493801{col 26}{space 2} .0908599{col 37}{space 1}    6.05{col 46}{space 3}0.000{col 54}{space 4} .3705107{col 67}{space 3} .7282495
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .4373213{col 26}{space 2} .0868943{col 37}{space 1}    5.03{col 46}{space 3}0.000{col 54}{space 4} .2662587{col 67}{space 3} .6083839
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .4221909{col 26}{space 2} .0872143{col 37}{space 1}    4.84{col 46}{space 3}0.000{col 54}{space 4} .2504985{col 67}{space 3} .5938834
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .5524713{col 26}{space 2} .1843659{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .1895234{col 67}{space 3} .9154191
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .4457137{col 26}{space 2} .1427238{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .1647436{col 67}{space 3} .7266839
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5300612{col 26}{space 2} .1262345{col 37}{space 1}    4.20{col 46}{space 3}0.000{col 54}{space 4} .2815525{col 67}{space 3} .7785699
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .5176555{col 26}{space 2} .1077434{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .3055488{col 67}{space 3} .7297622
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .4990399{col 26}{space 2} .1117195{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .2791057{col 67}{space 3} .7189741
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .2089239{col 26}{space 2} .1876216{col 37}{space 1}    1.11{col 46}{space 3}0.266{col 54}{space 4}-.1604333{col 67}{space 3} .5782811
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .2269243{col 26}{space 2} .1097011{col 37}{space 1}    2.07{col 46}{space 3}0.040{col 54}{space 4} .0109636{col 67}{space 3}  .442885
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .3069481{col 26}{space 2} .1120749{col 37}{space 1}    2.74{col 46}{space 3}0.007{col 54}{space 4} .0863143{col 67}{space 3} .5275819
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .2134827{col 26}{space 2} .1203003{col 37}{space 1}    1.77{col 46}{space 3}0.077{col 54}{space 4}-.0233439{col 67}{space 3} .4503092
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .2683529{col 26}{space 2} .0964696{col 37}{space 1}    2.78{col 46}{space 3}0.006{col 54}{space 4} .0784401{col 67}{space 3} .4582657
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5114151{col 26}{space 2} .1178969{col 37}{space 1}    4.34{col 46}{space 3}0.000{col 54}{space 4}   .27932{col 67}{space 3} .7435102
{txt}{space 9}62  {c |}{col 14}{res}{space 2}  .414632{col 26}{space 2} .1144206{col 37}{space 1}    3.62{col 46}{space 3}0.000{col 54}{space 4} .1893805{col 67}{space 3} .6398835
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .4120382{col 26}{space 2} .1113485{col 37}{space 1}    3.70{col 46}{space 3}0.000{col 54}{space 4} .1928344{col 67}{space 3}  .631242
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .3215455{col 26}{space 2} .1124931{col 37}{space 1}    2.86{col 46}{space 3}0.005{col 54}{space 4} .1000885{col 67}{space 3} .5430025
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .2659104{col 26}{space 2} .0968992{col 37}{space 1}    2.74{col 46}{space 3}0.006{col 54}{space 4} .0751519{col 67}{space 3}  .456669
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .5444506{col 26}{space 2} .1765348{col 37}{space 1}    3.08{col 46}{space 3}0.002{col 54}{space 4} .1969193{col 67}{space 3} .8919819
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .4756071{col 26}{space 2} .1216863{col 37}{space 1}    3.91{col 46}{space 3}0.000{col 54}{space 4} .2360521{col 67}{space 3} .7151621
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .5678494{col 26}{space 2} .1172559{col 37}{space 1}    4.84{col 46}{space 3}0.000{col 54}{space 4} .3370161{col 67}{space 3} .7986827
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6116019{col 26}{space 2} .1366452{col 37}{space 1}    4.48{col 46}{space 3}0.000{col 54}{space 4} .3425983{col 67}{space 3} .8806055
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4849152{col 26}{space 2} .1297961{col 37}{space 1}    3.74{col 46}{space 3}0.000{col 54}{space 4}  .229395{col 67}{space 3} .7404354
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4045935{col 26}{space 2} .1009195{col 37}{space 1}   -4.01{col 46}{space 3}0.000{col 54}{space 4}-.6032665{col 67}{space 3}-.2059204
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,261
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       413
{txt}{col 49}F({res}  72{txt},{res}    179{txt}){col 67}= {res} 107636.74
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1242
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0955
{txt}{col 49}Root MSE{col 67}= {res}    0.4754

{txt}{ralign 78:(Std. Err. adjusted for {res:180} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2} .0097925{col 26}{space 2} .0096334{col 37}{space 1}    1.02{col 46}{space 3}0.311{col 54}{space 4}-.0092172{col 67}{space 3} .0288022
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0030742{col 26}{space 2} .0070078{col 37}{space 1}    0.44{col 46}{space 3}0.661{col 54}{space 4}-.0107544{col 67}{space 3} .0169027
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0196502{col 26}{space 2} .0018739{col 37}{space 1}   10.49{col 46}{space 3}0.000{col 54}{space 4} .0159524{col 67}{space 3}  .023348
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002257{col 26}{space 2} .0000205{col 37}{space 1}  -11.02{col 46}{space 3}0.000{col 54}{space 4}-.0002661{col 67}{space 3}-.0001853
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0070803{col 26}{space 2} .0120197{col 37}{space 1}   -0.59{col 46}{space 3}0.557{col 54}{space 4}-.0307989{col 67}{space 3} .0166384
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0370741{col 26}{space 2} .0163071{col 37}{space 1}    2.27{col 46}{space 3}0.024{col 54}{space 4} .0048952{col 67}{space 3} .0692531
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0168176{col 26}{space 2} .0063732{col 37}{space 1}    2.64{col 46}{space 3}0.009{col 54}{space 4} .0042413{col 67}{space 3}  .029394
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0856637{col 26}{space 2}  .011409{col 37}{space 1}    7.51{col 46}{space 3}0.000{col 54}{space 4} .0631502{col 67}{space 3} .1081771
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .0986234{col 26}{space 2} .1510791{col 37}{space 1}    0.65{col 46}{space 3}0.515{col 54}{space 4}-.1995017{col 67}{space 3} .3967486
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0824709{col 26}{space 2} .1085028{col 37}{space 1}   -0.76{col 46}{space 3}0.448{col 54}{space 4}-.2965802{col 67}{space 3} .1316383
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0023779{col 26}{space 2} .0978828{col 37}{space 1}   -0.02{col 46}{space 3}0.981{col 54}{space 4}-.1955307{col 67}{space 3} .1907748
{txt}{space 9}10  {c |}{col 14}{res}{space 2}  .025591{col 26}{space 2} .1035788{col 37}{space 1}    0.25{col 46}{space 3}0.805{col 54}{space 4}-.1788016{col 67}{space 3} .2299836
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4479491{col 26}{space 2} .0961174{col 37}{space 1}    4.66{col 46}{space 3}0.000{col 54}{space 4}   .25828{col 67}{space 3} .6376181
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .2996078{col 26}{space 2} .0962098{col 37}{space 1}    3.11{col 46}{space 3}0.002{col 54}{space 4} .1097565{col 67}{space 3} .4894591
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .1672051{col 26}{space 2} .1003384{col 37}{space 1}    1.67{col 46}{space 3}0.097{col 54}{space 4}-.0307933{col 67}{space 3} .3652035
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .3463544{col 26}{space 2} .1027171{col 37}{space 1}    3.37{col 46}{space 3}0.001{col 54}{space 4} .1436622{col 67}{space 3} .5490467
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .2716971{col 26}{space 2} .1197879{col 37}{space 1}    2.27{col 46}{space 3}0.025{col 54}{space 4} .0353191{col 67}{space 3} .5080752
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .4223719{col 26}{space 2} .0397894{col 37}{space 1}   10.62{col 46}{space 3}0.000{col 54}{space 4} .3438553{col 67}{space 3} .5008885
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3920016{col 26}{space 2} .0297636{col 37}{space 1}   13.17{col 46}{space 3}0.000{col 54}{space 4} .3332689{col 67}{space 3} .4507342
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .3895565{col 26}{space 2} .0278862{col 37}{space 1}   13.97{col 46}{space 3}0.000{col 54}{space 4} .3345286{col 67}{space 3} .4445844
{txt}{space 9}19  {c |}{col 14}{res}{space 2}  .270829{col 26}{space 2} .0500614{col 37}{space 1}    5.41{col 46}{space 3}0.000{col 54}{space 4} .1720426{col 67}{space 3} .3696154
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .3165393{col 26}{space 2} .0609141{col 37}{space 1}    5.20{col 46}{space 3}0.000{col 54}{space 4} .1963371{col 67}{space 3} .4367415
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .2391683{col 26}{space 2}  .037168{col 37}{space 1}    6.43{col 46}{space 3}0.000{col 54}{space 4} .1658244{col 67}{space 3} .3125121
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .1346678{col 26}{space 2} .0685348{col 37}{space 1}    1.96{col 46}{space 3}0.051{col 54}{space 4}-.0005723{col 67}{space 3} .2699079
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .3412706{col 26}{space 2}  .071591{col 37}{space 1}    4.77{col 46}{space 3}0.000{col 54}{space 4} .1999997{col 67}{space 3} .4825415
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .3079385{col 26}{space 2} .0984033{col 37}{space 1}    3.13{col 46}{space 3}0.002{col 54}{space 4} .1137587{col 67}{space 3} .5021184
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .2720856{col 26}{space 2} .0495725{col 37}{space 1}    5.49{col 46}{space 3}0.000{col 54}{space 4} .1742638{col 67}{space 3} .3699074
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .3438497{col 26}{space 2}  .041277{col 37}{space 1}    8.33{col 46}{space 3}0.000{col 54}{space 4} .2623977{col 67}{space 3} .4253017
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .3339403{col 26}{space 2} .0339848{col 37}{space 1}    9.83{col 46}{space 3}0.000{col 54}{space 4}  .266878{col 67}{space 3} .4010026
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .2482307{col 26}{space 2} .0264472{col 37}{space 1}    9.39{col 46}{space 3}0.000{col 54}{space 4} .1960422{col 67}{space 3} .3004191
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .1683709{col 26}{space 2} .0282202{col 37}{space 1}    5.97{col 46}{space 3}0.000{col 54}{space 4} .1126837{col 67}{space 3}  .224058
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1164177{col 26}{space 2} .0343536{col 37}{space 1}    3.39{col 46}{space 3}0.001{col 54}{space 4} .0486276{col 67}{space 3} .1842078
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .3305583{col 26}{space 2} .0427428{col 37}{space 1}    7.73{col 46}{space 3}0.000{col 54}{space 4} .2462138{col 67}{space 3} .4149029
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .3309689{col 26}{space 2} .0454833{col 37}{space 1}    7.28{col 46}{space 3}0.000{col 54}{space 4} .2412164{col 67}{space 3} .4207214
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .3236455{col 26}{space 2} .0272126{col 37}{space 1}   11.89{col 46}{space 3}0.000{col 54}{space 4} .2699468{col 67}{space 3} .3773442
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .1869754{col 26}{space 2} .0253646{col 37}{space 1}    7.37{col 46}{space 3}0.000{col 54}{space 4} .1369234{col 67}{space 3} .2370274
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .1906457{col 26}{space 2} .0276625{col 37}{space 1}    6.89{col 46}{space 3}0.000{col 54}{space 4} .1360592{col 67}{space 3} .2452323
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .3324984{col 26}{space 2} .0498694{col 37}{space 1}    6.67{col 46}{space 3}0.000{col 54}{space 4} .2340909{col 67}{space 3} .4309059
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .1522718{col 26}{space 2} .1273916{col 37}{space 1}    1.20{col 46}{space 3}0.234{col 54}{space 4}-.0991107{col 67}{space 3} .4036542
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .4111621{col 26}{space 2} .0519965{col 37}{space 1}    7.91{col 46}{space 3}0.000{col 54}{space 4}  .308557{col 67}{space 3} .5137671
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .2673825{col 26}{space 2} .0702857{col 37}{space 1}    3.80{col 46}{space 3}0.000{col 54}{space 4} .1286873{col 67}{space 3} .4060776
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .2445965{col 26}{space 2} .0455442{col 37}{space 1}    5.37{col 46}{space 3}0.000{col 54}{space 4} .1547238{col 67}{space 3} .3344692
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .2989134{col 26}{space 2} .0261962{col 37}{space 1}   11.41{col 46}{space 3}0.000{col 54}{space 4} .2472203{col 67}{space 3} .3506065
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .2750945{col 26}{space 2} .0231516{col 37}{space 1}   11.88{col 46}{space 3}0.000{col 54}{space 4} .2294093{col 67}{space 3} .3207797
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .2597933{col 26}{space 2} .0252182{col 37}{space 1}   10.30{col 46}{space 3}0.000{col 54}{space 4}   .21003{col 67}{space 3} .3095566
{txt}{space 9}44  {c |}{col 14}{res}{space 2}  .158591{col 26}{space 2} .0459556{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .0679065{col 67}{space 3} .2492755
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .1755498{col 26}{space 2} .0462201{col 37}{space 1}    3.80{col 46}{space 3}0.000{col 54}{space 4} .0843435{col 67}{space 3} .2667561
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .4210873{col 26}{space 2} .0580718{col 37}{space 1}    7.25{col 46}{space 3}0.000{col 54}{space 4} .3064939{col 67}{space 3} .5356807
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .3765149{col 26}{space 2} .0344232{col 37}{space 1}   10.94{col 46}{space 3}0.000{col 54}{space 4} .3085875{col 67}{space 3} .4444423
{txt}{space 9}48  {c |}{col 14}{res}{space 2}  .273512{col 26}{space 2} .0400376{col 37}{space 1}    6.83{col 46}{space 3}0.000{col 54}{space 4} .1945057{col 67}{space 3} .3525184
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .1728838{col 26}{space 2} .0363831{col 37}{space 1}    4.75{col 46}{space 3}0.000{col 54}{space 4} .1010887{col 67}{space 3} .2446788
{txt}{space 9}50  {c |}{col 14}{res}{space 2}  .281762{col 26}{space 2} .0599692{col 37}{space 1}    4.70{col 46}{space 3}0.000{col 54}{space 4} .1634245{col 67}{space 3} .4000996
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .4996773{col 26}{space 2} .0652177{col 37}{space 1}    7.66{col 46}{space 3}0.000{col 54}{space 4} .3709829{col 67}{space 3} .6283718
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .3563497{col 26}{space 2} .0783673{col 37}{space 1}    4.55{col 46}{space 3}0.000{col 54}{space 4} .2017071{col 67}{space 3} .5109922
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .3011194{col 26}{space 2} .0440879{col 37}{space 1}    6.83{col 46}{space 3}0.000{col 54}{space 4} .2141204{col 67}{space 3} .3881183
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .3183908{col 26}{space 2} .0706227{col 37}{space 1}    4.51{col 46}{space 3}0.000{col 54}{space 4} .1790306{col 67}{space 3}  .457751
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .4433928{col 26}{space 2} .0640352{col 37}{space 1}    6.92{col 46}{space 3}0.000{col 54}{space 4} .3170319{col 67}{space 3} .5697537
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .0775992{col 26}{space 2} .0372535{col 37}{space 1}    2.08{col 46}{space 3}0.039{col 54}{space 4} .0040868{col 67}{space 3} .1511117
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .0068086{col 26}{space 2} .0351496{col 37}{space 1}    0.19{col 46}{space 3}0.847{col 54}{space 4}-.0625523{col 67}{space 3} .0761694
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .0338197{col 26}{space 2} .0414778{col 37}{space 1}    0.82{col 46}{space 3}0.416{col 54}{space 4}-.0480286{col 67}{space 3} .1156681
{txt}{space 9}59  {c |}{col 14}{res}{space 2}-.0408505{col 26}{space 2} .0430464{col 37}{space 1}   -0.95{col 46}{space 3}0.344{col 54}{space 4}-.1257942{col 67}{space 3} .0440933
{txt}{space 9}60  {c |}{col 14}{res}{space 2}-.0092351{col 26}{space 2} .0366921{col 37}{space 1}   -0.25{col 46}{space 3}0.802{col 54}{space 4}-.0816399{col 67}{space 3} .0631696
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .2511116{col 26}{space 2} .0602114{col 37}{space 1}    4.17{col 46}{space 3}0.000{col 54}{space 4} .1322962{col 67}{space 3}  .369927
{txt}{space 9}62  {c |}{col 14}{res}{space 2}  .191369{col 26}{space 2} .0389613{col 37}{space 1}    4.91{col 46}{space 3}0.000{col 54}{space 4} .1144864{col 67}{space 3} .2682515
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .2120797{col 26}{space 2} .0545179{col 37}{space 1}    3.89{col 46}{space 3}0.000{col 54}{space 4} .1044993{col 67}{space 3} .3196601
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .1655267{col 26}{space 2} .0356005{col 37}{space 1}    4.65{col 46}{space 3}0.000{col 54}{space 4}  .095276{col 67}{space 3} .2357774
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .0443596{col 26}{space 2} .0581504{col 37}{space 1}    0.76{col 46}{space 3}0.447{col 54}{space 4}-.0703889{col 67}{space 3}  .159108
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .4628332{col 26}{space 2} .0896678{col 37}{space 1}    5.16{col 46}{space 3}0.000{col 54}{space 4} .2858913{col 67}{space 3} .6397752
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .2704038{col 26}{space 2} .1125196{col 37}{space 1}    2.40{col 46}{space 3}0.017{col 54}{space 4} .0483683{col 67}{space 3} .4924393
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .3149555{col 26}{space 2} .0499694{col 37}{space 1}    6.30{col 46}{space 3}0.000{col 54}{space 4} .2163506{col 67}{space 3} .4135604
{txt}{space 9}69  {c |}{col 14}{res}{space 2}  .308704{col 26}{space 2} .0423036{col 37}{space 1}    7.30{col 46}{space 3}0.000{col 54}{space 4} .2252261{col 67}{space 3} .3921819
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .2344152{col 26}{space 2} .0607106{col 37}{space 1}    3.86{col 46}{space 3}0.000{col 54}{space 4} .1146146{col 67}{space 3} .3542158
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2181126{col 26}{space 2} .0665137{col 37}{space 1}   -3.28{col 46}{space 3}0.001{col 54}{space 4}-.3493644{col 67}{space 3}-.0868607
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    14,883
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       551
{txt}{col 49}F({res}  77{txt},{res}    212{txt}){col 67}= {res}    170.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1414
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1037
{txt}{col 49}Root MSE{col 67}= {res}    0.4733

{txt}{ralign 78:(Std. Err. adjusted for {res:213} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2}  .025409{col 26}{space 2} .0087957{col 37}{space 1}    2.89{col 46}{space 3}0.004{col 54}{space 4} .0080707{col 67}{space 3} .0427473
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0117326{col 26}{space 2} .0063406{col 37}{space 1}    1.85{col 46}{space 3}0.066{col 54}{space 4}-.0007662{col 67}{space 3} .0242314
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0155248{col 26}{space 2} .0016882{col 37}{space 1}    9.20{col 46}{space 3}0.000{col 54}{space 4} .0121969{col 67}{space 3} .0188526
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001803{col 26}{space 2}  .000019{col 37}{space 1}   -9.49{col 46}{space 3}0.000{col 54}{space 4}-.0002178{col 67}{space 3}-.0001429
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0086077{col 26}{space 2} .0091985{col 37}{space 1}    0.94{col 46}{space 3}0.350{col 54}{space 4}-.0095245{col 67}{space 3} .0267399
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0860315{col 26}{space 2} .0201115{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} .0463874{col 67}{space 3} .1256755
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0138968{col 26}{space 2} .0082595{col 37}{space 1}    1.68{col 46}{space 3}0.094{col 54}{space 4}-.0023844{col 67}{space 3}  .030178
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0813273{col 26}{space 2} .0093677{col 37}{space 1}    8.68{col 46}{space 3}0.000{col 54}{space 4} .0628616{col 67}{space 3} .0997931
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .4023803{col 26}{space 2} .3271236{col 37}{space 1}    1.23{col 46}{space 3}0.220{col 54}{space 4}-.2424512{col 67}{space 3} 1.047212
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .3860448{col 26}{space 2} .2537977{col 37}{space 1}    1.52{col 46}{space 3}0.130{col 54}{space 4}-.1142455{col 67}{space 3} .8863352
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2479151{col 26}{space 2} .2748185{col 37}{space 1}    0.90{col 46}{space 3}0.368{col 54}{space 4}-.2938117{col 67}{space 3} .7896419
{txt}{space 10}5  {c |}{col 14}{res}{space 2}  .231237{col 26}{space 2} .2300571{col 37}{space 1}    1.01{col 46}{space 3}0.316{col 54}{space 4}-.2222556{col 67}{space 3} .6847295
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2885908{col 26}{space 2} .0997072{col 37}{space 1}    2.89{col 46}{space 3}0.004{col 54}{space 4} .0920464{col 67}{space 3} .4851353
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .3252566{col 26}{space 2} .0942741{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .1394218{col 67}{space 3} .5110914
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2591495{col 26}{space 2} .0947762{col 37}{space 1}    2.73{col 46}{space 3}0.007{col 54}{space 4} .0723249{col 67}{space 3}  .445974
{txt}{space 10}9  {c |}{col 14}{res}{space 2}  .210496{col 26}{space 2} .0869752{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .0390491{col 67}{space 3} .3819429
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2337591{col 26}{space 2} .0813687{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0733638{col 67}{space 3} .3941544
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4817674{col 26}{space 2} .1292217{col 37}{space 1}    3.73{col 46}{space 3}0.000{col 54}{space 4} .2270435{col 67}{space 3} .7364913
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .4775315{col 26}{space 2}  .123896{col 37}{space 1}    3.85{col 46}{space 3}0.000{col 54}{space 4} .2333056{col 67}{space 3} .7217575
{txt}{space 9}13  {c |}{col 14}{res}{space 2}  .603211{col 26}{space 2} .1196446{col 37}{space 1}    5.04{col 46}{space 3}0.000{col 54}{space 4} .3673656{col 67}{space 3} .8390565
{txt}{space 9}14  {c |}{col 14}{res}{space 2}  .557779{col 26}{space 2}  .100849{col 37}{space 1}    5.53{col 46}{space 3}0.000{col 54}{space 4} .3589837{col 67}{space 3} .7565744
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{txt}{space 9}22  {c |}{col 14}{res}{space 2} .5281352{col 26}{space 2} .0857246{col 37}{space 1}    6.16{col 46}{space 3}0.000{col 54}{space 4} .3591534{col 67}{space 3}  .697117
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{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5175968{col 26}{space 2}  .094001{col 37}{space 1}    5.51{col 46}{space 3}0.000{col 54}{space 4} .3323004{col 67}{space 3} .7028932
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{txt}{space 9}44  {c |}{col 14}{res}{space 2} .7512132{col 26}{space 2} .2193279{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} .3188704{col 67}{space 3} 1.183556
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .7676801{col 26}{space 2} .2129512{col 37}{space 1}    3.60{col 46}{space 3}0.000{col 54}{space 4}  .347907{col 67}{space 3} 1.187453
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .3065108{col 26}{space 2} .1667306{col 37}{space 1}    1.84{col 46}{space 3}0.067{col 54}{space 4}-.0221515{col 67}{space 3} .6351731
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .5807616{col 26}{space 2} .1513191{col 37}{space 1}    3.84{col 46}{space 3}0.000{col 54}{space 4} .2824788{col 67}{space 3} .8790445
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .5141613{col 26}{space 2} .1346598{col 37}{space 1}    3.82{col 46}{space 3}0.000{col 54}{space 4} .2487176{col 67}{space 3}  .779605
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{txt}{space 9}50  {c |}{col 14}{res}{space 2} .4999313{col 26}{space 2} .1642014{col 37}{space 1}    3.04{col 46}{space 3}0.003{col 54}{space 4} .1762547{col 67}{space 3} .8236079
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .5548387{col 26}{space 2} .0795794{col 37}{space 1}    6.97{col 46}{space 3}0.000{col 54}{space 4} .3979704{col 67}{space 3} .7117071
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5165157{col 26}{space 2} .0855525{col 37}{space 1}    6.04{col 46}{space 3}0.000{col 54}{space 4} .3478732{col 67}{space 3} .6851582
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .4659013{col 26}{space 2} .0842725{col 37}{space 1}    5.53{col 46}{space 3}0.000{col 54}{space 4}  .299782{col 67}{space 3} .6320206
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .4425003{col 26}{space 2} .0878566{col 37}{space 1}    5.04{col 46}{space 3}0.000{col 54}{space 4} .2693158{col 67}{space 3} .6156848
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .4763292{col 26}{space 2} .0903845{col 37}{space 1}    5.27{col 46}{space 3}0.000{col 54}{space 4} .2981618{col 67}{space 3} .6544966
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .2544769{col 26}{space 2} .0899619{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .0771423{col 67}{space 3} .4318114
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .2740172{col 26}{space 2} .0982109{col 37}{space 1}    2.79{col 46}{space 3}0.006{col 54}{space 4} .0804222{col 67}{space 3} .4676123
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .2210973{col 26}{space 2} .0964674{col 37}{space 1}    2.29{col 46}{space 3}0.023{col 54}{space 4}  .030939{col 67}{space 3} .4112555
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .2523721{col 26}{space 2} .0867594{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0813504{col 67}{space 3} .4233937
{txt}{space 9}60  {c |}{col 14}{res}{space 2}  .225766{col 26}{space 2}  .090438{col 37}{space 1}    2.50{col 46}{space 3}0.013{col 54}{space 4}  .047493{col 67}{space 3}  .404039
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5119324{col 26}{space 2} .0984479{col 37}{space 1}    5.20{col 46}{space 3}0.000{col 54}{space 4} .3178703{col 67}{space 3} .7059945
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .5005394{col 26}{space 2} .1067537{col 37}{space 1}    4.69{col 46}{space 3}0.000{col 54}{space 4} .2901047{col 67}{space 3} .7109741
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .3706259{col 26}{space 2} .1164756{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4} .1410273{col 67}{space 3} .6002245
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .4418309{col 26}{space 2} .1164582{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4} .2122666{col 67}{space 3} .6713953
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{txt}{space 9}66  {c |}{col 14}{res}{space 2} .6161886{col 26}{space 2} .0940361{col 37}{space 1}    6.55{col 46}{space 3}0.000{col 54}{space 4}  .430823{col 67}{space 3} .8015542
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .5092215{col 26}{space 2} .0930747{col 37}{space 1}    5.47{col 46}{space 3}0.000{col 54}{space 4}  .325751{col 67}{space 3} .6926919
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .6068788{col 26}{space 2} .1003941{col 37}{space 1}    6.04{col 46}{space 3}0.000{col 54}{space 4} .4089802{col 67}{space 3} .8047774
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .4600045{col 26}{space 2} .0942998{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .2741191{col 67}{space 3} .6458899
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4500395{col 26}{space 2} .0945305{col 37}{space 1}    4.76{col 46}{space 3}0.000{col 54}{space 4} .2636994{col 67}{space 3} .6363796
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4304231{col 26}{space 2} .0911392{col 37}{space 1}   -4.72{col 46}{space 3}0.000{col 54}{space 4}-.6100782{col 67}{space 3}-.2507679
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_logs.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(logcoethnics) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_logs.tex"'})

{com}. 
. 
. ///NOW COMBINE: FUNCTIONAL FORM AND SINGLE COUNTRY
> preserve 
{txt}
{com}. keep if singlecountries==1
{txt}(35,637 observations deleted)

{com}. 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    26,657
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       723
{txt}{col 49}F({res}  27{txt},{res}    268{txt}){col 67}= {res}     74.26
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1141
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0884
{txt}{col 49}Root MSE{col 67}= {res}    0.4764

{txt}{ralign 78:(Std. Err. adjusted for {res:269} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2} .0017252{col 26}{space 2} .0067651{col 37}{space 1}    0.26{col 46}{space 3}0.799{col 54}{space 4}-.0115944{col 67}{space 3} .0150448
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0337722{col 26}{space 2}   .00773{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} .0185529{col 67}{space 3} .0489915
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0151246{col 26}{space 2} .0012809{col 37}{space 1}   11.81{col 46}{space 3}0.000{col 54}{space 4} .0126027{col 67}{space 3} .0176465
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001913{col 26}{space 2} .0000147{col 37}{space 1}  -13.03{col 46}{space 3}0.000{col 54}{space 4}-.0002202{col 67}{space 3}-.0001624
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0271527{col 26}{space 2} .0099341{col 37}{space 1}    2.73{col 46}{space 3}0.007{col 54}{space 4} .0075939{col 67}{space 3} .0467115
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0667059{col 26}{space 2} .0152401{col 37}{space 1}    4.38{col 46}{space 3}0.000{col 54}{space 4} .0367004{col 67}{space 3} .0967114
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0129049{col 26}{space 2} .0069552{col 37}{space 1}    1.86{col 46}{space 3}0.065{col 54}{space 4}-.0007888{col 67}{space 3} .0265987
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0838561{col 26}{space 2} .0074728{col 37}{space 1}   11.22{col 46}{space 3}0.000{col 54}{space 4} .0691432{col 67}{space 3} .0985689
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .0368838{col 26}{space 2} .0439181{col 37}{space 1}    0.84{col 46}{space 3}0.402{col 54}{space 4}-.0495846{col 67}{space 3} .1233521
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0205638{col 26}{space 2}  .042374{col 37}{space 1}   -0.49{col 46}{space 3}0.628{col 54}{space 4}-.1039921{col 67}{space 3} .0628645
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0564905{col 26}{space 2}   .03929{col 37}{space 1}   -1.44{col 46}{space 3}0.152{col 54}{space 4}-.1338469{col 67}{space 3} .0208659
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0385973{col 26}{space 2} .0520583{col 37}{space 1}   -0.74{col 46}{space 3}0.459{col 54}{space 4}-.1410925{col 67}{space 3} .0638979
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .1874851{col 26}{space 2} .0450242{col 37}{space 1}    4.16{col 46}{space 3}0.000{col 54}{space 4}  .098839{col 67}{space 3} .2761312
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .2041915{col 26}{space 2} .0469523{col 37}{space 1}    4.35{col 46}{space 3}0.000{col 54}{space 4} .1117492{col 67}{space 3} .2966339
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .1403263{col 26}{space 2} .0510561{col 37}{space 1}    2.75{col 46}{space 3}0.006{col 54}{space 4} .0398042{col 67}{space 3} .2408484
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .1010795{col 26}{space 2} .0484999{col 37}{space 1}    2.08{col 46}{space 3}0.038{col 54}{space 4} .0055902{col 67}{space 3} .1965689
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .1055926{col 26}{space 2} .0501995{col 37}{space 1}    2.10{col 46}{space 3}0.036{col 54}{space 4} .0067571{col 67}{space 3} .2044281
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .1939895{col 26}{space 2} .0588302{col 37}{space 1}    3.30{col 46}{space 3}0.001{col 54}{space 4} .0781614{col 67}{space 3} .3098177
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .1854428{col 26}{space 2} .0605158{col 37}{space 1}    3.06{col 46}{space 3}0.002{col 54}{space 4} .0662959{col 67}{space 3} .3045896
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .1972564{col 26}{space 2}  .056386{col 37}{space 1}    3.50{col 46}{space 3}0.001{col 54}{space 4} .0862404{col 67}{space 3} .3082723
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .1145712{col 26}{space 2} .0465817{col 37}{space 1}    2.46{col 46}{space 3}0.015{col 54}{space 4} .0228586{col 67}{space 3} .2062838
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .1102281{col 26}{space 2} .0560914{col 37}{space 1}    1.97{col 46}{space 3}0.050{col 54}{space 4}-.0002077{col 67}{space 3}  .220664
{txt}{space 9}51  {c |}{col 14}{res}{space 2}  .281995{col 26}{space 2} .0552891{col 37}{space 1}    5.10{col 46}{space 3}0.000{col 54}{space 4} .1731387{col 67}{space 3} .3908513
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .2214818{col 26}{space 2} .0653182{col 37}{space 1}    3.39{col 46}{space 3}0.001{col 54}{space 4} .0928798{col 67}{space 3} .3500838
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .1663468{col 26}{space 2}  .052254{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4} .0634663{col 67}{space 3} .2692272
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .1453497{col 26}{space 2} .0590003{col 37}{space 1}    2.46{col 46}{space 3}0.014{col 54}{space 4} .0291866{col 67}{space 3} .2615127
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .1586665{col 26}{space 2}  .056195{col 37}{space 1}    2.82{col 46}{space 3}0.005{col 54}{space 4} .0480266{col 67}{space 3} .2693063
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}   .02231{col 26}{space 2} .0513728{col 37}{space 1}    0.43{col 46}{space 3}0.664{col 54}{space 4}-.0788357{col 67}{space 3} .1234556
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     8,310
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       538
{txt}{col 49}F({res}  27{txt},{res}    243{txt}){col 67}= {res}     20.79
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1364
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0736
{txt}{col 49}Root MSE{col 67}= {res}    0.4742

{txt}{ralign 78:(Std. Err. adjusted for {res:244} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2}-.0166223{col 26}{space 2} .0111056{col 37}{space 1}   -1.50{col 46}{space 3}0.136{col 54}{space 4}-.0384979{col 67}{space 3} .0052532
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0400108{col 26}{space 2} .0119158{col 37}{space 1}    3.36{col 46}{space 3}0.001{col 54}{space 4} .0165395{col 67}{space 3} .0634822
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0203999{col 26}{space 2} .0029385{col 37}{space 1}    6.94{col 46}{space 3}0.000{col 54}{space 4} .0146118{col 67}{space 3}  .026188
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002695{col 26}{space 2} .0000346{col 37}{space 1}   -7.78{col 46}{space 3}0.000{col 54}{space 4}-.0003377{col 67}{space 3}-.0002013
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0449095{col 26}{space 2} .0188146{col 37}{space 1}    2.39{col 46}{space 3}0.018{col 54}{space 4} .0078489{col 67}{space 3} .0819701
{txt}{space 6}parent {c |}{col 14}{res}{space 2}  .052602{col 26}{space 2} .0232996{col 37}{space 1}    2.26{col 46}{space 3}0.025{col 54}{space 4}  .006707{col 67}{space 3}  .098497
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0137898{col 26}{space 2} .0088144{col 37}{space 1}    1.56{col 46}{space 3}0.119{col 54}{space 4}-.0035726{col 67}{space 3} .0311521
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0701555{col 26}{space 2} .0136355{col 37}{space 1}    5.15{col 46}{space 3}0.000{col 54}{space 4} .0432967{col 67}{space 3} .0970144
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .6214525{col 26}{space 2} .1308478{col 37}{space 1}    4.75{col 46}{space 3}0.000{col 54}{space 4} .3637118{col 67}{space 3} .8791932
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2644665{col 26}{space 2} .1092271{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .0493138{col 67}{space 3} .4796192
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2405191{col 26}{space 2} .1358251{col 37}{space 1}    1.77{col 46}{space 3}0.078{col 54}{space 4}-.0270258{col 67}{space 3} .5080639
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1361932{col 26}{space 2} .1082819{col 37}{space 1}    1.26{col 46}{space 3}0.210{col 54}{space 4}-.0770977{col 67}{space 3} .3494842
{txt}{space 9}41  {c |}{col 14}{res}{space 2}   .51652{col 26}{space 2} .0653793{col 37}{space 1}    7.90{col 46}{space 3}0.000{col 54}{space 4} .3877374{col 67}{space 3} .6453025
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .5527916{col 26}{space 2} .0637533{col 37}{space 1}    8.67{col 46}{space 3}0.000{col 54}{space 4}  .427212{col 67}{space 3} .6783711
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .4502126{col 26}{space 2}  .065693{col 37}{space 1}    6.85{col 46}{space 3}0.000{col 54}{space 4} .3208122{col 67}{space 3}  .579613
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .4346657{col 26}{space 2} .0646904{col 37}{space 1}    6.72{col 46}{space 3}0.000{col 54}{space 4} .3072402{col 67}{space 3} .5620912
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .4517935{col 26}{space 2} .0691539{col 37}{space 1}    6.53{col 46}{space 3}0.000{col 54}{space 4} .3155758{col 67}{space 3} .5880111
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .4947969{col 26}{space 2} .0784795{col 37}{space 1}    6.30{col 46}{space 3}0.000{col 54}{space 4} .3402101{col 67}{space 3} .6493837
{txt}{space 9}47  {c |}{col 14}{res}{space 2}  .461174{col 26}{space 2} .0631706{col 37}{space 1}    7.30{col 46}{space 3}0.000{col 54}{space 4} .3367421{col 67}{space 3} .5856059
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .5076973{col 26}{space 2} .0683878{col 37}{space 1}    7.42{col 46}{space 3}0.000{col 54}{space 4} .3729888{col 67}{space 3} .6424057
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .4202229{col 26}{space 2} .0610017{col 37}{space 1}    6.89{col 46}{space 3}0.000{col 54}{space 4} .3000633{col 67}{space 3} .5403825
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .3941041{col 26}{space 2}   .06104{col 37}{space 1}    6.46{col 46}{space 3}0.000{col 54}{space 4}  .273869{col 67}{space 3} .5143391
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .6584306{col 26}{space 2} .0976157{col 37}{space 1}    6.75{col 46}{space 3}0.000{col 54}{space 4} .4661496{col 67}{space 3} .8507116
{txt}{space 9}52  {c |}{col 14}{res}{space 2}  .531714{col 26}{space 2} .1204385{col 37}{space 1}    4.41{col 46}{space 3}0.000{col 54}{space 4} .2944774{col 67}{space 3} .7689507
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5398668{col 26}{space 2} .1012584{col 37}{space 1}    5.33{col 46}{space 3}0.000{col 54}{space 4} .3404105{col 67}{space 3} .7393231
{txt}{space 9}54  {c |}{col 14}{res}{space 2}  .442609{col 26}{space 2} .0847604{col 37}{space 1}    5.22{col 46}{space 3}0.000{col 54}{space 4} .2756501{col 67}{space 3} .6095678
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .4179341{col 26}{space 2} .0860329{col 37}{space 1}    4.86{col 46}{space 3}0.000{col 54}{space 4} .2484688{col 67}{space 3} .5873994
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2934927{col 26}{space 2} .0706973{col 37}{space 1}   -4.15{col 46}{space 3}0.000{col 54}{space 4}-.4327504{col 67}{space 3}-.1542351
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     8,916
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       535
{txt}{col 49}{help j_robustsingular##|_new:F(  25,    240)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1346
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0766
{txt}{col 49}Root MSE{col 67}= {res}    0.4782

{txt}{ralign 78:(Std. Err. adjusted for {res:241} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2}-.0025753{col 26}{space 2}  .012488{col 37}{space 1}   -0.21{col 46}{space 3}0.837{col 54}{space 4}-.0271753{col 67}{space 3} .0220247
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0484998{col 26}{space 2} .0115698{col 37}{space 1}    4.19{col 46}{space 3}0.000{col 54}{space 4} .0257085{col 67}{space 3} .0712911
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0132857{col 26}{space 2} .0027837{col 37}{space 1}    4.77{col 46}{space 3}0.000{col 54}{space 4} .0078021{col 67}{space 3} .0187694
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001882{col 26}{space 2} .0000303{col 37}{space 1}   -6.21{col 46}{space 3}0.000{col 54}{space 4}-.0002479{col 67}{space 3}-.0001284
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0718204{col 26}{space 2} .0180741{col 37}{space 1}    3.97{col 46}{space 3}0.000{col 54}{space 4} .0362163{col 67}{space 3} .1074246
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0481449{col 26}{space 2} .0253922{col 37}{space 1}    1.90{col 46}{space 3}0.059{col 54}{space 4}-.0018751{col 67}{space 3} .0981649
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0139688{col 26}{space 2} .0128415{col 37}{space 1}    1.09{col 46}{space 3}0.278{col 54}{space 4}-.0113277{col 67}{space 3} .0392654
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0889308{col 26}{space 2} .0119704{col 37}{space 1}    7.43{col 46}{space 3}0.000{col 54}{space 4} .0653504{col 67}{space 3} .1125112
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2} .6362729{col 26}{space 2} .2121604{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .2183386{col 67}{space 3} 1.054207
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0708846{col 26}{space 2}  .051169{col 37}{space 1}    1.39{col 46}{space 3}0.167{col 54}{space 4}-.0299131{col 67}{space 3} .1716823
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0611197{col 26}{space 2} .1142901{col 37}{space 1}    0.53{col 46}{space 3}0.593{col 54}{space 4}-.1640202{col 67}{space 3} .2862596
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .4208341{col 26}{space 2} .1128976{col 37}{space 1}    3.73{col 46}{space 3}0.000{col 54}{space 4} .1984373{col 67}{space 3} .6432308
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .4544412{col 26}{space 2}  .110993{col 37}{space 1}    4.09{col 46}{space 3}0.000{col 54}{space 4} .2357964{col 67}{space 3}  .673086
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .4014499{col 26}{space 2} .1128766{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .1790946{col 67}{space 3} .6238051
{txt}{space 9}44  {c |}{col 14}{res}{space 2}  .377955{col 26}{space 2} .1129167{col 37}{space 1}    3.35{col 46}{space 3}0.001{col 54}{space 4} .1555205{col 67}{space 3} .6003894
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .3272155{col 26}{space 2} .1138738{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .1028958{col 67}{space 3} .5515353
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .3449471{col 26}{space 2} .1406453{col 37}{space 1}    2.45{col 46}{space 3}0.015{col 54}{space 4} .0678901{col 67}{space 3}  .622004
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .3933263{col 26}{space 2} .1258177{col 37}{space 1}    3.13{col 46}{space 3}0.002{col 54}{space 4} .1454783{col 67}{space 3} .6411744
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .4557573{col 26}{space 2} .1151918{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4} .2288413{col 67}{space 3} .6826733
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .3377588{col 26}{space 2} .1138111{col 37}{space 1}    2.97{col 46}{space 3}0.003{col 54}{space 4} .1135627{col 67}{space 3} .5619549
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .3323367{col 26}{space 2} .1172503{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .1013656{col 67}{space 3} .5633078
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .4333965{col 26}{space 2} .1928398{col 37}{space 1}    2.25{col 46}{space 3}0.026{col 54}{space 4} .0535218{col 67}{space 3} .8132713
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .3894744{col 26}{space 2} .1478659{col 37}{space 1}    2.63{col 46}{space 3}0.009{col 54}{space 4} .0981938{col 67}{space 3} .6807551
{txt}{space 9}53  {c |}{col 14}{res}{space 2}  .476788{col 26}{space 2} .1509472{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4} .1794375{col 67}{space 3} .7741385
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .4092882{col 26}{space 2} .1285586{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4} .1560408{col 67}{space 3} .6625355
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .3452202{col 26}{space 2} .1266508{col 37}{space 1}    2.73{col 46}{space 3}0.007{col 54}{space 4} .0957312{col 67}{space 3} .5947092
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1775337{col 26}{space 2} .1210821{col 37}{space 1}   -1.47{col 46}{space 3}0.144{col 54}{space 4} -.416053{col 67}{space 3} .0609856
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     4,652
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       215
{txt}{col 49}F({res}  27{txt},{res}    105{txt}){col 67}= {res}    140.50
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1338
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0865
{txt}{col 49}Root MSE{col 67}= {res}    0.4778

{txt}{ralign 78:(Std. Err. adjusted for {res:106} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2}-.0037342{col 26}{space 2} .0181468{col 37}{space 1}   -0.21{col 46}{space 3}0.837{col 54}{space 4}-.0397161{col 67}{space 3} .0322476
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0195762{col 26}{space 2} .0111754{col 37}{space 1}    1.75{col 46}{space 3}0.083{col 54}{space 4}-.0025826{col 67}{space 3}  .041735
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0185867{col 26}{space 2} .0023463{col 37}{space 1}    7.92{col 46}{space 3}0.000{col 54}{space 4} .0139344{col 67}{space 3} .0232391
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002158{col 26}{space 2} .0000278{col 37}{space 1}   -7.75{col 46}{space 3}0.000{col 54}{space 4} -.000271{col 67}{space 3}-.0001606
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0474226{col 26}{space 2} .0182858{col 37}{space 1}   -2.59{col 46}{space 3}0.011{col 54}{space 4}-.0836799{col 67}{space 3}-.0111652
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0550716{col 26}{space 2} .0502369{col 37}{space 1}    1.10{col 46}{space 3}0.275{col 54}{space 4} -.044539{col 67}{space 3} .1546821
{txt}{space 4}children {c |}{col 14}{res}{space 2}  .018558{col 26}{space 2} .0172458{col 37}{space 1}    1.08{col 46}{space 3}0.284{col 54}{space 4}-.0156372{col 67}{space 3} .0527532
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0913827{col 26}{space 2} .0255324{col 37}{space 1}    3.58{col 46}{space 3}0.001{col 54}{space 4} .0407568{col 67}{space 3} .1420087
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .1178752{col 26}{space 2} .1424398{col 37}{space 1}    0.83{col 46}{space 3}0.410{col 54}{space 4}-.1645565{col 67}{space 3}  .400307
{txt}{space 10}8  {c |}{col 14}{res}{space 2} -.090639{col 26}{space 2} .1390469{col 37}{space 1}   -0.65{col 46}{space 3}0.516{col 54}{space 4}-.3663433{col 67}{space 3} .1850653
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0193706{col 26}{space 2} .0747383{col 37}{space 1}   -0.26{col 46}{space 3}0.796{col 54}{space 4}-.1675628{col 67}{space 3} .1288216
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0497258{col 26}{space 2} .2226289{col 37}{space 1}    0.22{col 46}{space 3}0.824{col 54}{space 4}-.3917062{col 67}{space 3} .4911577
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .2637437{col 26}{space 2} .0467828{col 37}{space 1}    5.64{col 46}{space 3}0.000{col 54}{space 4}  .170982{col 67}{space 3} .3565053
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .2500906{col 26}{space 2} .0443233{col 37}{space 1}    5.64{col 46}{space 3}0.000{col 54}{space 4} .1622056{col 67}{space 3} .3379756
{txt}{space 9}43  {c |}{col 14}{res}{space 2}  .246667{col 26}{space 2}   .04683{col 37}{space 1}    5.27{col 46}{space 3}0.000{col 54}{space 4} .1538117{col 67}{space 3} .3395223
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .1370841{col 26}{space 2} .0581993{col 37}{space 1}    2.36{col 46}{space 3}0.020{col 54}{space 4} .0216856{col 67}{space 3} .2524826
{txt}{space 9}45  {c |}{col 14}{res}{space 2}  .155498{col 26}{space 2} .0573879{col 37}{space 1}    2.71{col 46}{space 3}0.008{col 54}{space 4} .0417084{col 67}{space 3} .2692876
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .4168677{col 26}{space 2} .0858621{col 37}{space 1}    4.86{col 46}{space 3}0.000{col 54}{space 4} .2466191{col 67}{space 3} .5871163
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .3517081{col 26}{space 2} .0621152{col 37}{space 1}    5.66{col 46}{space 3}0.000{col 54}{space 4}  .228545{col 67}{space 3} .4748711
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .2605759{col 26}{space 2} .0633295{col 37}{space 1}    4.11{col 46}{space 3}0.000{col 54}{space 4} .1350052{col 67}{space 3} .3861467
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .1663714{col 26}{space 2} .0660321{col 37}{space 1}    2.52{col 46}{space 3}0.013{col 54}{space 4} .0354419{col 67}{space 3} .2973008
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .2789143{col 26}{space 2} .0858849{col 37}{space 1}    3.25{col 46}{space 3}0.002{col 54}{space 4} .1086205{col 67}{space 3} .4492081
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .4086615{col 26}{space 2} .0589862{col 37}{space 1}    6.93{col 46}{space 3}0.000{col 54}{space 4} .2917028{col 67}{space 3} .5256203
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .3187219{col 26}{space 2}  .078599{col 37}{space 1}    4.06{col 46}{space 3}0.000{col 54}{space 4} .1628746{col 67}{space 3} .4745693
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .2788942{col 26}{space 2} .0617842{col 37}{space 1}    4.51{col 46}{space 3}0.000{col 54}{space 4} .1563876{col 67}{space 3} .4014008
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .2829304{col 26}{space 2} .0775723{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .1291189{col 67}{space 3} .4367419
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .3903961{col 26}{space 2} .0776086{col 37}{space 1}    5.03{col 46}{space 3}0.000{col 54}{space 4} .2365127{col 67}{space 3} .5442795
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1041615{col 26}{space 2} .1208366{col 37}{space 1}   -0.86{col 46}{space 3}0.391{col 54}{space 4}-.3437581{col 67}{space 3} .1354351
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k logcoethnics $cov i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     4,779
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       405
{txt}{col 49}F({res}  27{txt},{res}    170{txt}){col 67}= {res}     19.59
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.2142
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1363
{txt}{col 49}Root MSE{col 67}= {res}    0.4599

{txt}{ralign 78:(Std. Err. adjusted for {res:171} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
logcoethnics {c |}{col 14}{res}{space 2} .0488779{col 26}{space 2} .0196283{col 37}{space 1}    2.49{col 46}{space 3}0.014{col 54}{space 4} .0101313{col 67}{space 3} .0876244
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0080539{col 26}{space 2} .0105814{col 37}{space 1}    0.76{col 46}{space 3}0.448{col 54}{space 4} -.012834{col 67}{space 3} .0289417
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0137867{col 26}{space 2} .0028499{col 37}{space 1}    4.84{col 46}{space 3}0.000{col 54}{space 4}  .008161{col 67}{space 3} .0194124
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001655{col 26}{space 2} .0000311{col 37}{space 1}   -5.32{col 46}{space 3}0.000{col 54}{space 4} -.000227{col 67}{space 3}-.0001041
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0067521{col 26}{space 2} .0179706{col 37}{space 1}    0.38{col 46}{space 3}0.708{col 54}{space 4}-.0287221{col 67}{space 3} .0422264
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .1402197{col 26}{space 2} .0344462{col 37}{space 1}    4.07{col 46}{space 3}0.000{col 54}{space 4} .0722223{col 67}{space 3} .2082171
{txt}{space 4}children {c |}{col 14}{res}{space 2}-.0013384{col 26}{space 2} .0145829{col 37}{space 1}   -0.09{col 46}{space 3}0.927{col 54}{space 4}-.0301252{col 67}{space 3} .0274484
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0796816{col 26}{space 2} .0174202{col 37}{space 1}    4.57{col 46}{space 3}0.000{col 54}{space 4} .0452939{col 67}{space 3} .1140694
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .0198353{col 26}{space 2} .0509822{col 37}{space 1}    0.39{col 46}{space 3}0.698{col 54}{space 4}-.0808045{col 67}{space 3} .1204751
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0491905{col 26}{space 2} .0438084{col 37}{space 1}   -1.12{col 46}{space 3}0.263{col 54}{space 4} -.135669{col 67}{space 3} .0372881
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0768996{col 26}{space 2} .0386579{col 37}{space 1}   -1.99{col 46}{space 3}0.048{col 54}{space 4} -.153211{col 67}{space 3}-.0005883
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.0622142{col 26}{space 2} .0522308{col 37}{space 1}   -1.19{col 46}{space 3}0.235{col 54}{space 4}-.1653186{col 67}{space 3} .0408902
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .0290017{col 26}{space 2} .0889589{col 37}{space 1}    0.33{col 46}{space 3}0.745{col 54}{space 4}-.1466047{col 67}{space 3} .2046082
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .0492879{col 26}{space 2} .0788356{col 37}{space 1}    0.63{col 46}{space 3}0.533{col 54}{space 4}-.1063349{col 67}{space 3} .2049106
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .3411694{col 26}{space 2}  .194976{col 37}{space 1}    1.75{col 46}{space 3}0.082{col 54}{space 4}-.0437165{col 67}{space 3} .7260552
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .4199182{col 26}{space 2} .2481877{col 37}{space 1}    1.69{col 46}{space 3}0.092{col 54}{space 4}-.0700085{col 67}{space 3} .9098449
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .3343755{col 26}{space 2} .2783408{col 37}{space 1}    1.20{col 46}{space 3}0.231{col 54}{space 4}-.2150738{col 67}{space 3} .8838248
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .0656663{col 26}{space 2} .1532314{col 37}{space 1}    0.43{col 46}{space 3}0.669{col 54}{space 4}-.2368151{col 67}{space 3} .3681477
{txt}{space 9}47  {c |}{col 14}{res}{space 2}   .23003{col 26}{space 2}  .144438{col 37}{space 1}    1.59{col 46}{space 3}0.113{col 54}{space 4}-.0550931{col 67}{space 3} .5151532
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .2787843{col 26}{space 2} .1174338{col 37}{space 1}    2.37{col 46}{space 3}0.019{col 54}{space 4} .0469679{col 67}{space 3} .5106006
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .2908523{col 26}{space 2} .1568266{col 37}{space 1}    1.85{col 46}{space 3}0.065{col 54}{space 4}-.0187259{col 67}{space 3} .6004306
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .2182508{col 26}{space 2} .1770889{col 37}{space 1}    1.23{col 46}{space 3}0.219{col 54}{space 4}-.1313257{col 67}{space 3} .5678273
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .2651506{col 26}{space 2} .0561339{col 37}{space 1}    4.72{col 46}{space 3}0.000{col 54}{space 4} .1543413{col 67}{space 3} .3759598
{txt}{space 9}52  {c |}{col 14}{res}{space 2}  .226056{col 26}{space 2} .0676865{col 37}{space 1}    3.34{col 46}{space 3}0.001{col 54}{space 4} .0924417{col 67}{space 3} .3596704
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .1656091{col 26}{space 2} .0522635{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4} .0624402{col 67}{space 3} .2687781
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .1456348{col 26}{space 2} .0660257{col 37}{space 1}    2.21{col 46}{space 3}0.029{col 54}{space 4}  .015299{col 67}{space 3} .2759705
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .1604192{col 26}{space 2} .0536519{col 37}{space 1}    2.99{col 46}{space 3}0.003{col 54}{space 4} .0545095{col 67}{space 3} .2663289
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1978887{col 26}{space 2} .0790174{col 37}{space 1}   -2.50{col 46}{space 3}0.013{col 54}{space 4}-.3538703{col 67}{space 3}-.0419071
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_logandsingle.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(logcoethnics) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_logandsingle.tex"'})

{com}. 
. restore 
{txt}
{com}. 
. 
. ////////////////////////
> //TABLE A7 - LONG TERM EU-ELECTIONS
.  
. ///MERGE ON 2009 VOTING DATA 
> preserve
{txt}
{com}. merge 1:m LopNr using "D:\SCB_ConPol\Stata\VD\Valdelt_09", keepusing (LopNr e)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       6,792,004
{txt}{col 9}from master{col 30}{res}          16,102{txt}  (_merge==1)
{col 9}from using{col 30}{res}       6,775,902{txt}  (_merge==2)

{col 5}matched{col 30}{res}          47,271{txt}  (_merge==3)
{col 5}{hline 41}

{com}. keep if _merge==3
{txt}(6,792,004 observations deleted)

{com}. 
. 
. *check for duplicates - OK
. *duplicates report LopNr 
. 
. ///create voting variable
> 
. ///voted or not
> gen voted_2009 =0 if e==1
{txt}(15,291 missing values generated)

{com}. replace voted_2009=1 if voted_2009==.
{txt}(15,291 real changes made)

{com}. 
. label variable voted_2009  "voted in EU election 2009"
{txt}
{com}. 
. 
. ///Baseline reg
> eststo clear
{txt}
{com}. 
. eststo: areg voted_2009 stdcoshare $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    45,442
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       979
{txt}{col 49}F({res}  77{txt},{res}    285{txt}){col 67}= {res}     47.02
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0785
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0566
{txt}{col 49}Root MSE{col 67}= {res}    0.4556

{txt}{ralign 78:(Std. Err. adjusted for {res:286} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  voted_2009{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0039841{col 26}{space 2} .0061367{col 37}{space 1}   -0.65{col 46}{space 3}0.517{col 54}{space 4}-.0160631{col 67}{space 3} .0080949
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0064832{col 26}{space 2}  .003995{col 37}{space 1}    1.62{col 46}{space 3}0.106{col 54}{space 4}-.0013803{col 67}{space 3} .0143468
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0217249{col 26}{space 2}  .001101{col 37}{space 1}   19.73{col 46}{space 3}0.000{col 54}{space 4} .0195577{col 67}{space 3} .0238921
{txt}{space 8}age2 {c |}{col 14}{res}{space 2} -.000276{col 26}{space 2}  .000014{col 37}{space 1}  -19.75{col 46}{space 3}0.000{col 54}{space 4}-.0003035{col 67}{space 3}-.0002485
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0040513{col 26}{space 2} .0061754{col 37}{space 1}    0.66{col 46}{space 3}0.512{col 54}{space 4}-.0081038{col 67}{space 3} .0162064
{txt}{space 6}parent {c |}{col 14}{res}{space 2}  .025232{col 26}{space 2} .0080998{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .0092889{col 67}{space 3} .0411751
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0021328{col 26}{space 2} .0028844{col 37}{space 1}    0.74{col 46}{space 3}0.460{col 54}{space 4}-.0035446{col 67}{space 3} .0078102
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0467092{col 26}{space 2}  .006019{col 37}{space 1}    7.76{col 46}{space 3}0.000{col 54}{space 4} .0348619{col 67}{space 3} .0585565
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .1444104{col 26}{space 2} .2773672{col 37}{space 1}    0.52{col 46}{space 3}0.603{col 54}{space 4}-.4015376{col 67}{space 3} .6903585
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .3523741{col 26}{space 2} .1999419{col 37}{space 1}    1.76{col 46}{space 3}0.079{col 54}{space 4} -.041176{col 67}{space 3} .7459242
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0156512{col 26}{space 2} .2383114{col 37}{space 1}    0.07{col 46}{space 3}0.948{col 54}{space 4}-.4534225{col 67}{space 3} .4847249
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.2150396{col 26}{space 2} .1321234{col 37}{space 1}   -1.63{col 46}{space 3}0.105{col 54}{space 4} -.475101{col 67}{space 3} .0450218
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0169017{col 26}{space 2} .1449893{col 37}{space 1}   -0.12{col 46}{space 3}0.907{col 54}{space 4}-.3022874{col 67}{space 3} .2684841
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0025616{col 26}{space 2} .1369897{col 37}{space 1}   -0.02{col 46}{space 3}0.985{col 54}{space 4}-.2722014{col 67}{space 3} .2670783
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0440487{col 26}{space 2} .1438904{col 37}{space 1}   -0.31{col 46}{space 3}0.760{col 54}{space 4}-.3272714{col 67}{space 3}  .239174
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0281806{col 26}{space 2} .1375444{col 37}{space 1}   -0.20{col 46}{space 3}0.838{col 54}{space 4}-.2989124{col 67}{space 3} .2425512
{txt}{space 9}10  {c |}{col 14}{res}{space 2} -.043269{col 26}{space 2} .1356522{col 37}{space 1}   -0.32{col 46}{space 3}0.750{col 54}{space 4}-.3102763{col 67}{space 3} .2237383
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .2997172{col 26}{space 2}  .139847{col 37}{space 1}    2.14{col 46}{space 3}0.033{col 54}{space 4} .0244533{col 67}{space 3} .5749811
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .173818{col 26}{space 2} .1488151{col 37}{space 1}    1.17{col 46}{space 3}0.244{col 54}{space 4}-.1190982{col 67}{space 3} .4667341
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3238387{col 26}{space 2}  .142346{col 37}{space 1}    2.28{col 46}{space 3}0.024{col 54}{space 4} .0436559{col 67}{space 3} .6040215
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .2291135{col 26}{space 2} .1459731{col 37}{space 1}    1.57{col 46}{space 3}0.118{col 54}{space 4}-.0582087{col 67}{space 3} .5164358
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .2704145{col 26}{space 2}  .152709{col 37}{space 1}    1.77{col 46}{space 3}0.078{col 54}{space 4}-.0301662{col 67}{space 3} .5709951
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .2706771{col 26}{space 2} .1379022{col 37}{space 1}    1.96{col 46}{space 3}0.051{col 54}{space 4} -.000759{col 67}{space 3} .5421131
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .2597044{col 26}{space 2}  .141776{col 37}{space 1}    1.83{col 46}{space 3}0.068{col 54}{space 4}-.0193565{col 67}{space 3} .5387654
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .2356881{col 26}{space 2}  .143477{col 37}{space 1}    1.64{col 46}{space 3}0.102{col 54}{space 4} -.046721{col 67}{space 3} .5180971
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2351388{col 26}{space 2} .1437423{col 37}{space 1}    1.64{col 46}{space 3}0.103{col 54}{space 4}-.0477925{col 67}{space 3} .5180701
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .2965027{col 26}{space 2} .1635262{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0253696{col 67}{space 3}  .618375
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .1767986{col 26}{space 2} .1324157{col 37}{space 1}    1.34{col 46}{space 3}0.183{col 54}{space 4}-.0838381{col 67}{space 3} .4374354
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .2086274{col 26}{space 2} .1292056{col 37}{space 1}    1.61{col 46}{space 3}0.107{col 54}{space 4}-.0456909{col 67}{space 3} .4629456
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{txt}{space 9}26  {c |}{col 14}{res}{space 2} .0489315{col 26}{space 2} .1383521{col 37}{space 1}    0.35{col 46}{space 3}0.724{col 54}{space 4}-.2233901{col 67}{space 3} .3212531
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .0803876{col 26}{space 2} .1395445{col 37}{space 1}    0.58{col 46}{space 3}0.565{col 54}{space 4} -.194281{col 67}{space 3} .3550562
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .0525823{col 26}{space 2} .1356343{col 37}{space 1}    0.39{col 46}{space 3}0.699{col 54}{space 4}-.2143899{col 67}{space 3} .3195544
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .0104442{col 26}{space 2} .1431981{col 37}{space 1}    0.07{col 46}{space 3}0.942{col 54}{space 4}-.2714158{col 67}{space 3} .2923041
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .0158764{col 26}{space 2} .1385421{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-.2568191{col 67}{space 3}  .288572
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .0730464{col 26}{space 2} .1389437{col 37}{space 1}    0.53{col 46}{space 3}0.599{col 54}{space 4}-.2004396{col 67}{space 3} .3465323
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1871792{col 26}{space 2}   .14311{col 37}{space 1}   -1.31{col 46}{space 3}0.192{col 54}{space 4}-.4688659{col 67}{space 3} .0945075
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_2009 stdcoshare $cov i.cohortbycountry if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 56.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    12,295
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       773
{txt}{col 49}F({res}  73{txt},{res}    277{txt}){col 67}= {res}     19.44
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1189
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0539
{txt}{col 49}Root MSE{col 67}= {res}    0.4612

{txt}{ralign 78:(Std. Err. adjusted for {res:278} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  voted_2009{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0008553{col 26}{space 2} .0085205{col 37}{space 1}   -0.10{col 46}{space 3}0.920{col 54}{space 4}-.0176285{col 67}{space 3} .0159179
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0057366{col 26}{space 2} .0099572{col 37}{space 1}    0.58{col 46}{space 3}0.565{col 54}{space 4}-.0138648{col 67}{space 3}  .025338
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0250374{col 26}{space 2} .0024635{col 37}{space 1}   10.16{col 46}{space 3}0.000{col 54}{space 4} .0201879{col 67}{space 3}  .029887
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}  -.00031{col 26}{space 2}  .000034{col 37}{space 1}   -9.12{col 46}{space 3}0.000{col 54}{space 4} -.000377{col 67}{space 3}-.0002431
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0117747{col 26}{space 2} .0132284{col 37}{space 1}    0.89{col 46}{space 3}0.374{col 54}{space 4}-.0142662{col 67}{space 3} .0378156
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0211409{col 26}{space 2} .0165262{col 37}{space 1}    1.28{col 46}{space 3}0.202{col 54}{space 4} -.011392{col 67}{space 3} .0536738
{txt}{space 4}children {c |}{col 14}{res}{space 2}   .00105{col 26}{space 2} .0052235{col 37}{space 1}    0.20{col 46}{space 3}0.841{col 54}{space 4}-.0092328{col 67}{space 3} .0113329
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0324993{col 26}{space 2} .0112227{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4} .0104067{col 67}{space 3} .0545919
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} -.554148{col 26}{space 2} .0978745{col 37}{space 1}   -5.66{col 46}{space 3}0.000{col 54}{space 4}-.7468203{col 67}{space 3}-.3614758
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_2009 stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{txt}note: 4.cohortbycountry omitted because of collinearity
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    11,974
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       715
{txt}{col 49}{help j_robustsingular##|_new:F(  71,    274)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1198
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0578
{txt}{col 49}Root MSE{col 67}= {res}    0.4557

{txt}{ralign 78:(Std. Err. adjusted for {res:275} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  voted_2009{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0096419{col 26}{space 2} .0129489{col 37}{space 1}    0.74{col 46}{space 3}0.457{col 54}{space 4}-.0158502{col 67}{space 3}  .035134
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0104044{col 26}{space 2} .0091445{col 37}{space 1}    1.14{col 46}{space 3}0.256{col 54}{space 4} -.007598{col 67}{space 3} .0284068
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0224517{col 26}{space 2} .0021793{col 37}{space 1}   10.30{col 46}{space 3}0.000{col 54}{space 4} .0181614{col 67}{space 3} .0267419
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002989{col 26}{space 2} .0000273{col 37}{space 1}  -10.94{col 46}{space 3}0.000{col 54}{space 4}-.0003526{col 67}{space 3}-.0002451
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0113308{col 26}{space 2} .0126802{col 37}{space 1}    0.89{col 46}{space 3}0.372{col 54}{space 4}-.0136323{col 67}{space 3} .0362938
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0466176{col 26}{space 2}  .017596{col 37}{space 1}    2.65{col 46}{space 3}0.009{col 54}{space 4} .0119771{col 67}{space 3} .0812581
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0020688{col 26}{space 2} .0058494{col 37}{space 1}    0.35{col 46}{space 3}0.724{col 54}{space 4}-.0094466{col 67}{space 3} .0135842
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0438747{col 26}{space 2} .0127114{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .0188502{col 67}{space 3} .0688991
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2} .8814187{col 26}{space 2} .0439344{col 37}{space 1}   20.06{col 46}{space 3}0.000{col 54}{space 4} .7949269{col 67}{space 3} .9679105
{txt}{space 10}4  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}5  {c |}{col 14}{res}{space 2} .1641725{col 26}{space 2} .0320235{col 37}{space 1}    5.13{col 46}{space 3}0.000{col 54}{space 4} .1011292{col 67}{space 3} .2272159
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2791974{col 26}{space 2} .0412403{col 37}{space 1}    6.77{col 46}{space 3}0.000{col 54}{space 4} .1980092{col 67}{space 3} .3603856
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2} .0774731{col 26}{space 2} .0953911{col 37}{space 1}    0.81{col 46}{space 3}0.417{col 54}{space 4}-.1103194{col 67}{space 3} .2652657
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3581447{col 26}{space 2} .0688434{col 37}{space 1}    5.20{col 46}{space 3}0.000{col 54}{space 4} .2226154{col 67}{space 3} .4936739
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1334287{col 26}{space 2} .1019901{col 37}{space 1}    1.31{col 46}{space 3}0.192{col 54}{space 4}-.0673551{col 67}{space 3} .3342125
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .5938379{col 26}{space 2}  .121547{col 37}{space 1}    4.89{col 46}{space 3}0.000{col 54}{space 4} .3545533{col 67}{space 3} .8331225
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .2877121{col 26}{space 2} .0844156{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .1215266{col 67}{space 3} .4538976
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .4917382{col 26}{space 2} .1091888{col 37}{space 1}    4.50{col 46}{space 3}0.000{col 54}{space 4} .2767826{col 67}{space 3} .7066938
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .3758401{col 26}{space 2}  .100896{col 37}{space 1}    3.73{col 46}{space 3}0.000{col 54}{space 4} .1772103{col 67}{space 3}   .57447
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .4400642{col 26}{space 2} .1158363{col 37}{space 1}    3.80{col 46}{space 3}0.000{col 54}{space 4}  .212022{col 67}{space 3} .6681064
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .4124396{col 26}{space 2} .0617168{col 37}{space 1}    6.68{col 46}{space 3}0.000{col 54}{space 4} .2909402{col 67}{space 3}  .533939
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .4832526{col 26}{space 2} .0522783{col 37}{space 1}    9.24{col 46}{space 3}0.000{col 54}{space 4} .3803344{col 67}{space 3} .5861708
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .3933068{col 26}{space 2} .0438486{col 37}{space 1}    8.97{col 46}{space 3}0.000{col 54}{space 4} .3069838{col 67}{space 3} .4796298
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .3630904{col 26}{space 2}  .056404{col 37}{space 1}    6.44{col 46}{space 3}0.000{col 54}{space 4} .2520501{col 67}{space 3} .4741307
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .5378999{col 26}{space 2} .1577673{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .2273097{col 67}{space 3} .8484901
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.0937716{col 26}{space 2} .1388813{col 37}{space 1}   -0.68{col 46}{space 3}0.500{col 54}{space 4}-.3671817{col 67}{space 3} .1796384
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .2186389{col 26}{space 2} .2421199{col 37}{space 1}    0.90{col 46}{space 3}0.367{col 54}{space 4}-.2580127{col 67}{space 3} .6952905
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .4115005{col 26}{space 2} .1469013{col 37}{space 1}    2.80{col 46}{space 3}0.005{col 54}{space 4} .1223019{col 67}{space 3} .7006992
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .2209616{col 26}{space 2} .1424212{col 37}{space 1}    1.55{col 46}{space 3}0.122{col 54}{space 4}-.0594174{col 67}{space 3} .5013405
{txt}{space 9}25  {c |}{col 14}{res}{space 2}  .361827{col 26}{space 2} .1155806{col 37}{space 1}    3.13{col 46}{space 3}0.002{col 54}{space 4} .1342881{col 67}{space 3} .5893658
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .2162832{col 26}{space 2} .0435875{col 37}{space 1}    4.96{col 46}{space 3}0.000{col 54}{space 4} .1304742{col 67}{space 3} .3020922
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .2606789{col 26}{space 2} .0383555{col 37}{space 1}    6.80{col 46}{space 3}0.000{col 54}{space 4}   .18517{col 67}{space 3} .3361878
{txt}{space 9}28  {c |}{col 14}{res}{space 2}  .264864{col 26}{space 2} .0393355{col 37}{space 1}    6.73{col 46}{space 3}0.000{col 54}{space 4} .1874258{col 67}{space 3} .3423022
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .2077913{col 26}{space 2} .0360673{col 37}{space 1}    5.76{col 46}{space 3}0.000{col 54}{space 4}  .136787{col 67}{space 3} .2787957
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1856316{col 26}{space 2} .0408357{col 37}{space 1}    4.55{col 46}{space 3}0.000{col 54}{space 4} .1052399{col 67}{space 3} .2660233
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .2126602{col 26}{space 2} .0782154{col 37}{space 1}    2.72{col 46}{space 3}0.007{col 54}{space 4} .0586808{col 67}{space 3} .3666396
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .2364993{col 26}{space 2} .0572853{col 37}{space 1}    4.13{col 46}{space 3}0.000{col 54}{space 4} .1237241{col 67}{space 3} .3492746
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .2215324{col 26}{space 2} .0362677{col 37}{space 1}    6.11{col 46}{space 3}0.000{col 54}{space 4} .1501337{col 67}{space 3} .2929311
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .2439579{col 26}{space 2} .0349958{col 37}{space 1}    6.97{col 46}{space 3}0.000{col 54}{space 4}  .175063{col 67}{space 3} .3128528
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .2524351{col 26}{space 2} .0459454{col 37}{space 1}    5.49{col 46}{space 3}0.000{col 54}{space 4} .1619843{col 67}{space 3} .3428859
{txt}{space 9}36  {c |}{col 14}{res}{space 2}-.0011827{col 26}{space 2} .0612218{col 37}{space 1}   -0.02{col 46}{space 3}0.985{col 54}{space 4}-.1217075{col 67}{space 3} .1193421
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .3766851{col 26}{space 2} .1599578{col 37}{space 1}    2.35{col 46}{space 3}0.019{col 54}{space 4} .0617827{col 67}{space 3} .6915876
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .4128256{col 26}{space 2} .1748312{col 37}{space 1}    2.36{col 46}{space 3}0.019{col 54}{space 4} .0686425{col 67}{space 3} .7570087
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .1604684{col 26}{space 2} .1401097{col 37}{space 1}    1.15{col 46}{space 3}0.253{col 54}{space 4}  -.11536{col 67}{space 3} .4362968
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .3050237{col 26}{space 2} .1319091{col 37}{space 1}    2.31{col 46}{space 3}0.021{col 54}{space 4} .0453396{col 67}{space 3} .5647078
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .3252435{col 26}{space 2} .0321318{col 37}{space 1}   10.12{col 46}{space 3}0.000{col 54}{space 4} .2619869{col 67}{space 3} .3885002
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .3274019{col 26}{space 2} .0306551{col 37}{space 1}   10.68{col 46}{space 3}0.000{col 54}{space 4} .2670524{col 67}{space 3} .3877515
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .3223436{col 26}{space 2} .0320205{col 37}{space 1}   10.07{col 46}{space 3}0.000{col 54}{space 4} .2593062{col 67}{space 3}  .385381
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .3452401{col 26}{space 2} .0351375{col 37}{space 1}    9.83{col 46}{space 3}0.000{col 54}{space 4} .2760663{col 67}{space 3} .4144139
{txt}{space 9}45  {c |}{col 14}{res}{space 2}  .331907{col 26}{space 2} .0416972{col 37}{space 1}    7.96{col 46}{space 3}0.000{col 54}{space 4} .2498194{col 67}{space 3} .4139946
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .2685756{col 26}{space 2}  .072159{col 37}{space 1}    3.72{col 46}{space 3}0.000{col 54}{space 4} .1265191{col 67}{space 3} .4106321
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .3155066{col 26}{space 2} .0381556{col 37}{space 1}    8.27{col 46}{space 3}0.000{col 54}{space 4} .2403912{col 67}{space 3} .3906219
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .2974632{col 26}{space 2} .0380533{col 37}{space 1}    7.82{col 46}{space 3}0.000{col 54}{space 4} .2225492{col 67}{space 3} .3723772
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .2488539{col 26}{space 2} .0357302{col 37}{space 1}    6.96{col 46}{space 3}0.000{col 54}{space 4} .1785132{col 67}{space 3} .3191945
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .3137331{col 26}{space 2} .0407172{col 37}{space 1}    7.71{col 46}{space 3}0.000{col 54}{space 4} .2335747{col 67}{space 3} .3938915
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .4015183{col 26}{space 2} .1044235{col 37}{space 1}    3.85{col 46}{space 3}0.000{col 54}{space 4}  .195944{col 67}{space 3} .6070926
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .2856683{col 26}{space 2} .1215518{col 37}{space 1}    2.35{col 46}{space 3}0.019{col 54}{space 4} .0463742{col 67}{space 3} .5249625
{txt}{space 9}53  {c |}{col 14}{res}{space 2}  .483581{col 26}{space 2} .1124502{col 37}{space 1}    4.30{col 46}{space 3}0.000{col 54}{space 4} .2622049{col 67}{space 3} .7049571
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .4308144{col 26}{space 2} .0884526{col 37}{space 1}    4.87{col 46}{space 3}0.000{col 54}{space 4} .2566813{col 67}{space 3} .6049476
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .5919691{col 26}{space 2} .0786502{col 37}{space 1}    7.53{col 46}{space 3}0.000{col 54}{space 4} .4371336{col 67}{space 3} .7468047
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .1882158{col 26}{space 2} .2015767{col 37}{space 1}    0.93{col 46}{space 3}0.351{col 54}{space 4}  -.20862{col 67}{space 3} .5850516
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .0607288{col 26}{space 2} .0523304{col 37}{space 1}    1.16{col 46}{space 3}0.247{col 54}{space 4} -.042292{col 67}{space 3} .1637495
{txt}{space 9}58  {c |}{col 14}{res}{space 2}-.0250397{col 26}{space 2} .1336792{col 37}{space 1}   -0.19{col 46}{space 3}0.852{col 54}{space 4}-.2882086{col 67}{space 3} .2381291
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .0930153{col 26}{space 2} .0750446{col 37}{space 1}    1.24{col 46}{space 3}0.216{col 54}{space 4}-.0547219{col 67}{space 3} .2407525
{txt}{space 9}60  {c |}{col 14}{res}{space 2}-.1183761{col 26}{space 2} .0498797{col 37}{space 1}   -2.37{col 46}{space 3}0.018{col 54}{space 4}-.2165723{col 67}{space 3}  -.02018
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .2493778{col 26}{space 2} .0958376{col 37}{space 1}    2.60{col 46}{space 3}0.010{col 54}{space 4} .0607062{col 67}{space 3} .4380494
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .1757732{col 26}{space 2} .0510779{col 37}{space 1}    3.44{col 46}{space 3}0.001{col 54}{space 4} .0752183{col 67}{space 3} .2763282
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .1606495{col 26}{space 2} .0536113{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .0551071{col 67}{space 3} .2661919
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .1818203{col 26}{space 2} .0451836{col 37}{space 1}    4.02{col 46}{space 3}0.000{col 54}{space 4} .0928691{col 67}{space 3} .2707714
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .1589042{col 26}{space 2} .0504408{col 37}{space 1}    3.15{col 46}{space 3}0.002{col 54}{space 4} .0596034{col 67}{space 3} .2582051
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .1801414{col 26}{space 2} .1123024{col 37}{space 1}    1.60{col 46}{space 3}0.110{col 54}{space 4}-.0409439{col 67}{space 3} .4012267
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .2252078{col 26}{space 2} .1893643{col 37}{space 1}    1.19{col 46}{space 3}0.235{col 54}{space 4} -.147586{col 67}{space 3} .5980016
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .1081093{col 26}{space 2}  .079834{col 37}{space 1}    1.35{col 46}{space 3}0.177{col 54}{space 4}-.0490567{col 67}{space 3} .2652754
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .3932582{col 26}{space 2} .0970877{col 37}{space 1}    4.05{col 46}{space 3}0.000{col 54}{space 4} .2021255{col 67}{space 3} .5843909
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .3571267{col 26}{space 2} .1017522{col 37}{space 1}    3.51{col 46}{space 3}0.001{col 54}{space 4} .1568113{col 67}{space 3} .5574421
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3952082{col 26}{space 2} .0447705{col 37}{space 1}   -8.83{col 46}{space 3}0.000{col 54}{space 4}-.4833461{col 67}{space 3}-.3070703
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_2009 stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    11,095
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       393
{txt}{col 49}F({res}  72{txt},{res}    172{txt}){col 67}= {res}    399.67
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1089
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0700
{txt}{col 49}Root MSE{col 67}= {res}    0.4468

{txt}{ralign 78:(Std. Err. adjusted for {res:173} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  voted_2009{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0081811{col 26}{space 2} .0161737{col 37}{space 1}   -0.51{col 46}{space 3}0.614{col 54}{space 4}-.0401056{col 67}{space 3} .0237434
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0003531{col 26}{space 2} .0072096{col 37}{space 1}   -0.05{col 46}{space 3}0.961{col 54}{space 4}-.0145837{col 67}{space 3} .0138775
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0200311{col 26}{space 2} .0023465{col 37}{space 1}    8.54{col 46}{space 3}0.000{col 54}{space 4} .0153993{col 67}{space 3} .0246628
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002529{col 26}{space 2} .0000301{col 37}{space 1}   -8.40{col 46}{space 3}0.000{col 54}{space 4}-.0003123{col 67}{space 3}-.0001935
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0080996{col 26}{space 2} .0106424{col 37}{space 1}   -0.76{col 46}{space 3}0.448{col 54}{space 4}-.0291061{col 67}{space 3} .0129069
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0196177{col 26}{space 2} .0178028{col 37}{space 1}    1.10{col 46}{space 3}0.272{col 54}{space 4}-.0155225{col 67}{space 3} .0547578
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0033149{col 26}{space 2} .0064149{col 37}{space 1}    0.52{col 46}{space 3}0.606{col 54}{space 4}-.0093471{col 67}{space 3} .0159769
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0630723{col 26}{space 2} .0094453{col 37}{space 1}    6.68{col 46}{space 3}0.000{col 54}{space 4} .0444286{col 67}{space 3}  .081716
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .4753564{col 26}{space 2} .2914968{col 37}{space 1}    1.63{col 46}{space 3}0.105{col 54}{space 4}-.1000151{col 67}{space 3} 1.050728
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0719683{col 26}{space 2} .1671438{col 37}{space 1}    0.43{col 46}{space 3}0.667{col 54}{space 4}-.2579488{col 67}{space 3} .4018854
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1735043{col 26}{space 2} .1467949{col 37}{space 1}   -1.18{col 46}{space 3}0.239{col 54}{space 4}-.4632557{col 67}{space 3}  .116247
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_2009 stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    10,078
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       649
{txt}{col 49}F({res}  77{txt},{res}    238{txt}){col 67}= {res}     72.84
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1436
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0772
{txt}{col 49}Root MSE{col 67}= {res}    0.4499

{txt}{ralign 78:(Std. Err. adjusted for {res:239} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  voted_2009{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0038891{col 26}{space 2} .0058946{col 37}{space 1}   -0.66{col 46}{space 3}0.510{col 54}{space 4}-.0155015{col 67}{space 3} .0077232
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0133946{col 26}{space 2} .0133155{col 37}{space 1}    1.01{col 46}{space 3}0.315{col 54}{space 4}-.0128367{col 67}{space 3} .0396258
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0195629{col 26}{space 2}  .001763{col 37}{space 1}   11.10{col 46}{space 3}0.000{col 54}{space 4} .0160898{col 67}{space 3}  .023036
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002409{col 26}{space 2} .0000201{col 37}{space 1}  -11.99{col 46}{space 3}0.000{col 54}{space 4}-.0002805{col 67}{space 3}-.0002013
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0004965{col 26}{space 2} .0140437{col 37}{space 1}    0.04{col 46}{space 3}0.972{col 54}{space 4}-.0271694{col 67}{space 3} .0281624
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0218686{col 26}{space 2} .0208014{col 37}{space 1}    1.05{col 46}{space 3}0.294{col 54}{space 4}-.0191097{col 67}{space 3}  .062847
{txt}{space 4}children {c |}{col 14}{res}{space 2}-.0002538{col 26}{space 2} .0092518{col 37}{space 1}   -0.03{col 46}{space 3}0.978{col 54}{space 4}-.0184797{col 67}{space 3} .0179721
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0482138{col 26}{space 2} .0127428{col 37}{space 1}    3.78{col 46}{space 3}0.000{col 54}{space 4} .0231107{col 67}{space 3} .0733169
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0750189{col 26}{space 2} .3104149{col 37}{space 1}   -0.24{col 46}{space 3}0.809{col 54}{space 4}-.6865305{col 67}{space 3} .5364926
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.3603666{col 26}{space 2} .1475266{col 37}{space 1}   -2.44{col 46}{space 3}0.015{col 54}{space 4}-.6509913{col 67}{space 3}-.0697418
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.1898055{col 26}{space 2} .1569374{col 37}{space 1}   -1.21{col 46}{space 3}0.228{col 54}{space 4}-.4989693{col 67}{space 3} .1193583
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.1799004{col 26}{space 2} .1438824{col 37}{space 1}   -1.25{col 46}{space 3}0.212{col 54}{space 4}-.4633461{col 67}{space 3} .1035454
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.2263908{col 26}{space 2} .1503003{col 37}{space 1}   -1.51{col 46}{space 3}0.133{col 54}{space 4}-.5224796{col 67}{space 3}  .069698
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.2224795{col 26}{space 2} .1453211{col 37}{space 1}   -1.53{col 46}{space 3}0.127{col 54}{space 4}-.5087595{col 67}{space 3} .0638004
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.1735097{col 26}{space 2} .1447921{col 37}{space 1}   -1.20{col 46}{space 3}0.232{col 54}{space 4}-.4587475{col 67}{space 3} .1117282
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1499208{col 26}{space 2} .2408892{col 37}{space 1}    0.62{col 46}{space 3}0.534{col 54}{space 4}-.3246265{col 67}{space 3} .6244682
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0905075{col 26}{space 2} .1822801{col 37}{space 1}    0.50{col 46}{space 3}0.620{col 54}{space 4} -.268581{col 67}{space 3} .4495961
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .0703821{col 26}{space 2} .1956254{col 37}{space 1}    0.36{col 46}{space 3}0.719{col 54}{space 4}-.3149962{col 67}{space 3} .4557605
{txt}{space 9}14  {c |}{col 14}{res}{space 2}-.0221238{col 26}{space 2} .1753706{col 37}{space 1}   -0.13{col 46}{space 3}0.900{col 54}{space 4}-.3676007{col 67}{space 3}  .323353
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{txt}{space 9}16  {c |}{col 14}{res}{space 2} .0894206{col 26}{space 2} .1474748{col 37}{space 1}    0.61{col 46}{space 3}0.545{col 54}{space 4} -.201102{col 67}{space 3} .3799432
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .1117065{col 26}{space 2} .1517303{col 37}{space 1}    0.74{col 46}{space 3}0.462{col 54}{space 4}-.1871994{col 67}{space 3} .4106124
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .0928405{col 26}{space 2} .1531945{col 37}{space 1}    0.61{col 46}{space 3}0.545{col 54}{space 4}-.2089498{col 67}{space 3} .3946309
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .0863344{col 26}{space 2} .1555433{col 37}{space 1}    0.56{col 46}{space 3}0.579{col 54}{space 4}-.2200829{col 67}{space 3} .3927518
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .0967291{col 26}{space 2}  .200136{col 37}{space 1}    0.48{col 46}{space 3}0.629{col 54}{space 4}-.2975352{col 67}{space 3} .4909933
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .0078974{col 26}{space 2} .1465683{col 37}{space 1}    0.05{col 46}{space 3}0.957{col 54}{space 4}-.2808395{col 67}{space 3} .2966342
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .0670508{col 26}{space 2} .1510066{col 37}{space 1}    0.44{col 46}{space 3}0.657{col 54}{space 4}-.2304295{col 67}{space 3}  .364531
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .0099181{col 26}{space 2} .1596191{col 37}{space 1}    0.06{col 46}{space 3}0.951{col 54}{space 4}-.3045286{col 67}{space 3} .3243648
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0096262{col 26}{space 2}    .1585{col 37}{space 1}    0.06{col 46}{space 3}0.952{col 54}{space 4}-.3026159{col 67}{space 3} .3218683
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reglong_EU.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reglong_EU.tex"'})

{com}. 
. restore
{txt}
{com}. 
. 
. 
. 
. //////////////////////////////////////////////////////////////////////////////////////
> ///NOW TABLE A8 - ADDING CONTROL FOR MOVING OUT OF MUNICIPALITY
> 
. ///short run effect
> 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare $cov move_out_muni i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    61,155
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}     1,063
{txt}{col 49}F({res}  78{txt},{res}    285{txt}){col 67}= {res}    152.78
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1101
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0932
{txt}{col 49}Root MSE{col 67}= {res}    0.4759

{txt}{ralign 78:(Std. Err. adjusted for {res:286} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0082855{col 26}{space 2} .0068058{col 37}{space 1}    1.22{col 46}{space 3}0.224{col 54}{space 4}-.0051104{col 67}{space 3} .0216814
{txt}{space 9}sex {c |}{col 14}{res}{space 2}    .0164{col 26}{space 2} .0046954{col 37}{space 1}    3.49{col 46}{space 3}0.001{col 54}{space 4}  .007158{col 67}{space 3}  .025642
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0193729{col 26}{space 2} .0012717{col 37}{space 1}   15.23{col 46}{space 3}0.000{col 54}{space 4} .0168699{col 67}{space 3}  .021876
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002264{col 26}{space 2} .0000148{col 37}{space 1}  -15.31{col 46}{space 3}0.000{col 54}{space 4}-.0002555{col 67}{space 3}-.0001973
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0114674{col 26}{space 2} .0051666{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4} .0012978{col 67}{space 3}  .021637
{txt}{space 6}parent {c |}{col 14}{res}{space 2}  .050998{col 26}{space 2} .0085183{col 37}{space 1}    5.99{col 46}{space 3}0.000{col 54}{space 4} .0342312{col 67}{space 3} .0677648
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0146511{col 26}{space 2} .0035345{col 37}{space 1}    4.15{col 46}{space 3}0.000{col 54}{space 4} .0076941{col 67}{space 3} .0216081
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0779722{col 26}{space 2} .0056926{col 37}{space 1}   13.70{col 46}{space 3}0.000{col 54}{space 4} .0667673{col 67}{space 3} .0891772
{txt}move_out_m~i {c |}{col 14}{res}{space 2} .0456401{col 26}{space 2} .0073019{col 37}{space 1}    6.25{col 46}{space 3}0.000{col 54}{space 4} .0312676{col 67}{space 3} .0600125
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}45  {c |}{col 14}{res}{space 2} .2733488{col 26}{space 2} .1257055{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0259198{col 67}{space 3} .5207779
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{txt}{space 9}48  {c |}{col 14}{res}{space 2} .3679077{col 26}{space 2} .1297087{col 37}{space 1}    2.84{col 46}{space 3}0.005{col 54}{space 4} .1125991{col 67}{space 3} .6232163
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .2958523{col 26}{space 2}  .133895{col 37}{space 1}    2.21{col 46}{space 3}0.028{col 54}{space 4} .0323036{col 67}{space 3} .5594009
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .2788092{col 26}{space 2} .1262259{col 37}{space 1}    2.21{col 46}{space 3}0.028{col 54}{space 4}  .030356{col 67}{space 3} .5272624
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{txt}{space 9}52  {c |}{col 14}{res}{space 2} .3926149{col 26}{space 2} .1256737{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .1452486{col 67}{space 3} .6399812
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .3492851{col 26}{space 2}   .13397{col 37}{space 1}    2.61{col 46}{space 3}0.010{col 54}{space 4}  .085589{col 67}{space 3} .6129812
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .3242889{col 26}{space 2} .1273929{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} .0735385{col 67}{space 3} .5750393
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{txt}{space 9}59  {c |}{col 14}{res}{space 2} .0566177{col 26}{space 2} .1129512{col 37}{space 1}    0.50{col 46}{space 3}0.617{col 54}{space 4}-.1657067{col 67}{space 3} .2789422
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .0725052{col 26}{space 2} .1211963{col 37}{space 1}    0.60{col 46}{space 3}0.550{col 54}{space 4}-.1660483{col 67}{space 3} .3110586
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .3207677{col 26}{space 2} .1393493{col 37}{space 1}    2.30{col 46}{space 3}0.022{col 54}{space 4} .0464834{col 67}{space 3}  .595052
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .2604821{col 26}{space 2} .1372055{col 37}{space 1}    1.90{col 46}{space 3}0.059{col 54}{space 4}-.0095825{col 67}{space 3} .5305468
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .2313852{col 26}{space 2} .1432367{col 37}{space 1}    1.62{col 46}{space 3}0.107{col 54}{space 4}-.0505509{col 67}{space 3} .5133212
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .1930586{col 26}{space 2} .1320686{col 37}{space 1}    1.46{col 46}{space 3}0.145{col 54}{space 4} -.066895{col 67}{space 3} .4530122
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .1156102{col 26}{space 2}  .119347{col 37}{space 1}    0.97{col 46}{space 3}0.334{col 54}{space 4}-.1193032{col 67}{space 3} .3505236
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .4785209{col 26}{space 2} .1439388{col 37}{space 1}    3.32{col 46}{space 3}0.001{col 54}{space 4}  .195203{col 67}{space 3} .7618388
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .3380602{col 26}{space 2} .1347033{col 37}{space 1}    2.51{col 46}{space 3}0.013{col 54}{space 4} .0729206{col 67}{space 3} .6031997
{txt}{space 9}68  {c |}{col 14}{res}{space 2}  .424418{col 26}{space 2} .1378662{col 37}{space 1}    3.08{col 46}{space 3}0.002{col 54}{space 4} .1530527{col 67}{space 3} .6957832
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .3393396{col 26}{space 2} .1404689{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .0628515{col 67}{space 3} .6158278
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .2958513{col 26}{space 2} .1440332{col 37}{space 1}    2.05{col 46}{space 3}0.041{col 54}{space 4} .0123475{col 67}{space 3} .5793551
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2346835{col 26}{space 2} .1352166{col 37}{space 1}   -1.74{col 46}{space 3}0.084{col 54}{space 4}-.5008333{col 67}{space 3} .0314663
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov move_out_muni i.cohortbycountry if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 6.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,332
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       825
{txt}{col 49}F({res}  74{txt},{res}    277{txt}){col 67}= {res}     20.09
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1536
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1009
{txt}{col 49}Root MSE{col 67}= {res}    0.4715

{txt}{ralign 78:(Std. Err. adjusted for {res:278} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0021705{col 26}{space 2} .0097816{col 37}{space 1}   -0.22{col 46}{space 3}0.825{col 54}{space 4}-.0214262{col 67}{space 3} .0170852
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0265329{col 26}{space 2} .0087089{col 37}{space 1}    3.05{col 46}{space 3}0.003{col 54}{space 4} .0093888{col 67}{space 3} .0436771
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0252488{col 26}{space 2}    .0018{col 37}{space 1}   14.03{col 46}{space 3}0.000{col 54}{space 4} .0217054{col 67}{space 3} .0287922
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0003043{col 26}{space 2} .0000219{col 37}{space 1}  -13.89{col 46}{space 3}0.000{col 54}{space 4}-.0003475{col 67}{space 3}-.0002612
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0259197{col 26}{space 2} .0122147{col 37}{space 1}    2.12{col 46}{space 3}0.035{col 54}{space 4} .0018742{col 67}{space 3} .0499652
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0413477{col 26}{space 2} .0165398{col 37}{space 1}    2.50{col 46}{space 3}0.013{col 54}{space 4}  .008788{col 67}{space 3} .0739074
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0135244{col 26}{space 2} .0052956{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} .0030997{col 67}{space 3}  .023949
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0560057{col 26}{space 2} .0106379{col 37}{space 1}    5.26{col 46}{space 3}0.000{col 54}{space 4} .0350642{col 67}{space 3} .0769472
{txt}move_out_m~i {c |}{col 14}{res}{space 2} .0380489{col 26}{space 2} .0119818{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4}  .014462{col 67}{space 3} .0616357
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2} .0623904{col 26}{space 2} .3227637{col 37}{space 1}    0.19{col 46}{space 3}0.847{col 54}{space 4} -.572991{col 67}{space 3} .6977718
{txt}{space 10}6  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}7  {c |}{col 14}{res}{space 2} .5720577{col 26}{space 2} .3235863{col 37}{space 1}    1.77{col 46}{space 3}0.078{col 54}{space 4} -.064943{col 67}{space 3} 1.209059
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1661778{col 26}{space 2} .1777391{col 37}{space 1}    0.93{col 46}{space 3}0.351{col 54}{space 4}-.1837131{col 67}{space 3} .5160688
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3097453{col 26}{space 2} .1932767{col 37}{space 1}    1.60{col 46}{space 3}0.110{col 54}{space 4}-.0707325{col 67}{space 3} .6902232
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2760623{col 26}{space 2} .1785835{col 37}{space 1}    1.55{col 46}{space 3}0.123{col 54}{space 4} -.075491{col 67}{space 3} .6276156
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .7622167{col 26}{space 2}   .17043{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .4267141{col 67}{space 3} 1.097719
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .6249202{col 26}{space 2} .1643217{col 37}{space 1}    3.80{col 46}{space 3}0.000{col 54}{space 4} .3014422{col 67}{space 3} .9483981
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5922618{col 26}{space 2} .1662421{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .2650033{col 67}{space 3} .9195203
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5573833{col 26}{space 2} .1590958{col 37}{space 1}    3.50{col 46}{space 3}0.001{col 54}{space 4} .2441929{col 67}{space 3} .8705738
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .6446875{col 26}{space 2} .1610911{col 37}{space 1}    4.00{col 46}{space 3}0.000{col 54}{space 4} .3275693{col 67}{space 3} .9618058
{txt}{space 9}16  {c |}{col 14}{res}{space 2}  .747653{col 26}{space 2}  .146564{col 37}{space 1}    5.10{col 46}{space 3}0.000{col 54}{space 4} .4591323{col 67}{space 3} 1.036174
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .6770255{col 26}{space 2} .1450935{col 37}{space 1}    4.67{col 46}{space 3}0.000{col 54}{space 4} .3913996{col 67}{space 3} .9626514
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .6223212{col 26}{space 2} .1447574{col 37}{space 1}    4.30{col 46}{space 3}0.000{col 54}{space 4} .3373568{col 67}{space 3} .9072855
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .6036371{col 26}{space 2} .1519744{col 37}{space 1}    3.97{col 46}{space 3}0.000{col 54}{space 4} .3044655{col 67}{space 3} .9028087
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .4069368{col 26}{space 2} .1744961{col 37}{space 1}    2.33{col 46}{space 3}0.020{col 54}{space 4} .0634298{col 67}{space 3} .7504438
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .4944466{col 26}{space 2} .2043028{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .0922632{col 67}{space 3} .8966301
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .5252443{col 26}{space 2} .2159033{col 37}{space 1}    2.43{col 46}{space 3}0.016{col 54}{space 4} .1002247{col 67}{space 3} .9502639
{txt}{space 9}23  {c |}{col 14}{res}{space 2}  .645499{col 26}{space 2} .1715844{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} .3077239{col 67}{space 3} .9832742
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .4173925{col 26}{space 2} .1780553{col 37}{space 1}    2.34{col 46}{space 3}0.020{col 54}{space 4}  .066879{col 67}{space 3} .7679059
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5509479{col 26}{space 2} .1972041{col 37}{space 1}    2.79{col 46}{space 3}0.006{col 54}{space 4} .1627387{col 67}{space 3}  .939157
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .6532311{col 26}{space 2} .1436064{col 37}{space 1}    4.55{col 46}{space 3}0.000{col 54}{space 4} .3705325{col 67}{space 3} .9359298
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .6391274{col 26}{space 2} .1441594{col 37}{space 1}    4.43{col 46}{space 3}0.000{col 54}{space 4} .3553401{col 67}{space 3} .9229146
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .4901865{col 26}{space 2} .1462783{col 37}{space 1}    3.35{col 46}{space 3}0.001{col 54}{space 4}  .202228{col 67}{space 3} .7781449
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .4735827{col 26}{space 2} .1480309{col 37}{space 1}    3.20{col 46}{space 3}0.002{col 54}{space 4} .1821743{col 67}{space 3}  .764991
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .3905853{col 26}{space 2}   .14436{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1064033{col 67}{space 3} .6747673
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .6725885{col 26}{space 2} .1655879{col 37}{space 1}    4.06{col 46}{space 3}0.000{col 54}{space 4}  .346618{col 67}{space 3} .9985591
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .6152404{col 26}{space 2} .1468048{col 37}{space 1}    4.19{col 46}{space 3}0.000{col 54}{space 4} .3262456{col 67}{space 3} .9042352
{txt}{space 9}33  {c |}{col 14}{res}{space 2}  .564845{col 26}{space 2} .1467213{col 37}{space 1}    3.85{col 46}{space 3}0.000{col 54}{space 4} .2760147{col 67}{space 3} .8536754
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .4144263{col 26}{space 2} .1481266{col 37}{space 1}    2.80{col 46}{space 3}0.006{col 54}{space 4} .1228295{col 67}{space 3}  .706023
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .4079058{col 26}{space 2} .1476197{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} .1173067{col 67}{space 3} .6985048
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .5201854{col 26}{space 2} .2205523{col 37}{space 1}    2.36{col 46}{space 3}0.019{col 54}{space 4} .0860138{col 67}{space 3} .9543571
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .6102578{col 26}{space 2} .2056799{col 37}{space 1}    2.97{col 46}{space 3}0.003{col 54}{space 4} .2053636{col 67}{space 3} 1.015152
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{txt}{space 9}39  {c |}{col 14}{res}{space 2} .4690673{col 26}{space 2} .1982812{col 37}{space 1}    2.37{col 46}{space 3}0.019{col 54}{space 4} .0787379{col 67}{space 3} .8593967
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5774597{col 26}{space 2} .1586798{col 37}{space 1}    3.64{col 46}{space 3}0.000{col 54}{space 4} .2650882{col 67}{space 3} .8898311
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .6325215{col 26}{space 2} .1447934{col 37}{space 1}    4.37{col 46}{space 3}0.000{col 54}{space 4} .3474862{col 67}{space 3} .9175567
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .6542444{col 26}{space 2} .1448453{col 37}{space 1}    4.52{col 46}{space 3}0.000{col 54}{space 4} .3691071{col 67}{space 3} .9393818
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .5439225{col 26}{space 2} .1454905{col 37}{space 1}    3.74{col 46}{space 3}0.000{col 54}{space 4} .2575149{col 67}{space 3} .8303301
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .5269963{col 26}{space 2} .1456042{col 37}{space 1}    3.62{col 46}{space 3}0.000{col 54}{space 4} .2403651{col 67}{space 3} .8136276
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{txt}{space 9}50  {c |}{col 14}{res}{space 2} .4939022{col 26}{space 2} .1456675{col 37}{space 1}    3.39{col 46}{space 3}0.001{col 54}{space 4} .2071463{col 67}{space 3} .7806581
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .7293335{col 26}{space 2} .1651553{col 37}{space 1}    4.42{col 46}{space 3}0.000{col 54}{space 4} .4042145{col 67}{space 3} 1.054453
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{txt}{space 9}54  {c |}{col 14}{res}{space 2} .5410738{col 26}{space 2} .1475898{col 37}{space 1}    3.67{col 46}{space 3}0.000{col 54}{space 4} .2505337{col 67}{space 3}  .831614
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{txt}{space 9}56  {c |}{col 14}{res}{space 2} .3866227{col 26}{space 2} .1601366{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .0713834{col 67}{space 3} .7018619
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .5421743{col 26}{space 2} .1891421{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .1698356{col 67}{space 3} .9145129
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .3776601{col 26}{space 2} .1537807{col 37}{space 1}    2.46{col 46}{space 3}0.015{col 54}{space 4} .0749329{col 67}{space 3} .6803874
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .4032078{col 26}{space 2} .1750938{col 37}{space 1}    2.30{col 46}{space 3}0.022{col 54}{space 4} .0585242{col 67}{space 3} .7478914
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .3035852{col 26}{space 2} .1680488{col 37}{space 1}    1.81{col 46}{space 3}0.072{col 54}{space 4}-.0272297{col 67}{space 3} .6344002
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5011015{col 26}{space 2} .1772602{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .1521534{col 67}{space 3} .8500496
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{txt}{space 9}63  {c |}{col 14}{res}{space 2} .3207391{col 26}{space 2}  .152198{col 37}{space 1}    2.11{col 46}{space 3}0.036{col 54}{space 4} .0211274{col 67}{space 3} .6203508
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .4124379{col 26}{space 2} .1522892{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1126467{col 67}{space 3}  .712229
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .3778922{col 26}{space 2} .1472796{col 37}{space 1}    2.57{col 46}{space 3}0.011{col 54}{space 4} .0879628{col 67}{space 3} .6678217
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{txt}{space 9}67  {c |}{col 14}{res}{space 2} .5236458{col 26}{space 2} .1806406{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4}  .168043{col 67}{space 3} .8792486
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .7168196{col 26}{space 2} .1645015{col 37}{space 1}    4.36{col 46}{space 3}0.000{col 54}{space 4} .3929877{col 67}{space 3} 1.040652
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6154704{col 26}{space 2} .1721594{col 37}{space 1}    3.58{col 46}{space 3}0.000{col 54}{space 4} .2765635{col 67}{space 3} .9543773
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .5434389{col 26}{space 2} .1630846{col 37}{space 1}    3.33{col 46}{space 3}0.001{col 54}{space 4} .2223964{col 67}{space 3} .8644815
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.5510328{col 26}{space 2} .1442236{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.8349463{col 67}{space 3}-.2671192
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov move_out_muni i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,261
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       754
{txt}{col 49}{help j_robustsingular##|_new:F(  74,    275)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1441
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0949
{txt}{col 49}Root MSE{col 67}= {res}    0.4750

{txt}{ralign 78:(Std. Err. adjusted for {res:276} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0095346{col 26}{space 2} .0121445{col 37}{space 1}   -0.79{col 46}{space 3}0.433{col 54}{space 4}-.0334427{col 67}{space 3} .0143735
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0313933{col 26}{space 2} .0101352{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4} .0114409{col 67}{space 3} .0513457
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0205237{col 26}{space 2} .0027693{col 37}{space 1}    7.41{col 46}{space 3}0.000{col 54}{space 4} .0150719{col 67}{space 3} .0259755
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002509{col 26}{space 2} .0000297{col 37}{space 1}   -8.46{col 46}{space 3}0.000{col 54}{space 4}-.0003093{col 67}{space 3}-.0001925
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0301512{col 26}{space 2} .0131959{col 37}{space 1}    2.28{col 46}{space 3}0.023{col 54}{space 4} .0041734{col 67}{space 3}  .056129
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0460172{col 26}{space 2} .0172092{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4} .0121387{col 67}{space 3} .0798956
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0130263{col 26}{space 2} .0073445{col 37}{space 1}    1.77{col 46}{space 3}0.077{col 54}{space 4}-.0014322{col 67}{space 3} .0274849
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0812738{col 26}{space 2} .0112015{col 37}{space 1}    7.26{col 46}{space 3}0.000{col 54}{space 4} .0592223{col 67}{space 3} .1033253
{txt}move_out_m~i {c |}{col 14}{res}{space 2} .0421534{col 26}{space 2} .0127693{col 37}{space 1}    3.30{col 46}{space 3}0.001{col 54}{space 4} .0170154{col 67}{space 3} .0672915
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.2460602{col 26}{space 2}  .081286{col 37}{space 1}   -3.03{col 46}{space 3}0.003{col 54}{space 4}-.4060821{col 67}{space 3}-.0860383
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0079762{col 26}{space 2} .0816137{col 37}{space 1}    0.10{col 46}{space 3}0.922{col 54}{space 4}-.1526909{col 67}{space 3} .1686432
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0720853{col 26}{space 2} .0801379{col 37}{space 1}   -0.90{col 46}{space 3}0.369{col 54}{space 4} -.229847{col 67}{space 3} .0856765
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1633747{col 26}{space 2}   .10774{col 37}{space 1}    1.52{col 46}{space 3}0.131{col 54}{space 4}-.0487254{col 67}{space 3} .3754747
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2} .6561839{col 26}{space 2} .2315831{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .2002829{col 67}{space 3} 1.112085
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3143762{col 26}{space 2} .1465436{col 37}{space 1}    2.15{col 46}{space 3}0.033{col 54}{space 4} .0258864{col 67}{space 3} .6028661
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2097095{col 26}{space 2} .1388574{col 37}{space 1}    1.51{col 46}{space 3}0.132{col 54}{space 4}-.0636489{col 67}{space 3}  .483068
{txt}{space 9}11  {c |}{col 14}{res}{space 2}  .847885{col 26}{space 2} .1374568{col 37}{space 1}    6.17{col 46}{space 3}0.000{col 54}{space 4} .5772838{col 67}{space 3} 1.118486
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5693958{col 26}{space 2}  .102399{col 37}{space 1}    5.56{col 46}{space 3}0.000{col 54}{space 4} .3678103{col 67}{space 3} .7709813
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5653384{col 26}{space 2} .1254227{col 37}{space 1}    4.51{col 46}{space 3}0.000{col 54}{space 4} .3184278{col 67}{space 3}  .812249
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .6097969{col 26}{space 2} .0888134{col 37}{space 1}    6.87{col 46}{space 3}0.000{col 54}{space 4} .4349564{col 67}{space 3} .7846373
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .4935019{col 26}{space 2} .1467582{col 37}{space 1}    3.36{col 46}{space 3}0.001{col 54}{space 4} .2045896{col 67}{space 3} .7824142
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .6526735{col 26}{space 2} .0884079{col 37}{space 1}    7.38{col 46}{space 3}0.000{col 54}{space 4} .4786312{col 67}{space 3} .8267159
{txt}{space 9}17  {c |}{col 14}{res}{space 2}  .628162{col 26}{space 2}  .086555{col 37}{space 1}    7.26{col 46}{space 3}0.000{col 54}{space 4} .4577675{col 67}{space 3} .7985565
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{txt}{space 9}19  {c |}{col 14}{res}{space 2} .5565884{col 26}{space 2} .0977632{col 37}{space 1}    5.69{col 46}{space 3}0.000{col 54}{space 4}  .364129{col 67}{space 3} .7490478
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .6872908{col 26}{space 2} .1506577{col 37}{space 1}    4.56{col 46}{space 3}0.000{col 54}{space 4} .3907018{col 67}{space 3} .9838798
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .7427247{col 26}{space 2} .2088099{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .3316556{col 67}{space 3} 1.153794
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .7440575{col 26}{space 2} .2328817{col 37}{space 1}    3.20{col 46}{space 3}0.002{col 54}{space 4} .2856001{col 67}{space 3} 1.202515
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .5678261{col 26}{space 2} .1550625{col 37}{space 1}    3.66{col 46}{space 3}0.000{col 54}{space 4} .2625658{col 67}{space 3} .8730865
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{txt}{space 9}28  {c |}{col 14}{res}{space 2} .4459426{col 26}{space 2} .0868345{col 37}{space 1}    5.14{col 46}{space 3}0.000{col 54}{space 4} .2749979{col 67}{space 3} .6168873
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .3845534{col 26}{space 2} .0915449{col 37}{space 1}    4.20{col 46}{space 3}0.000{col 54}{space 4} .2043356{col 67}{space 3} .5647711
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .3036251{col 26}{space 2} .0963517{col 37}{space 1}    3.15{col 46}{space 3}0.002{col 54}{space 4} .1139446{col 67}{space 3} .4933057
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .6065986{col 26}{space 2} .1032996{col 37}{space 1}    5.87{col 46}{space 3}0.000{col 54}{space 4} .4032401{col 67}{space 3}  .809957
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4207488{col 26}{space 2} .1022157{col 37}{space 1}   -4.12{col 46}{space 3}0.000{col 54}{space 4}-.6219735{col 67}{space 3} -.219524
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov move_out_muni i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,261
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       413
{txt}{col 49}F({res}  73{txt},{res}    179{txt}){col 67}= {res}  21462.13
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1252
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0964
{txt}{col 49}Root MSE{col 67}= {res}    0.4752

{txt}{ralign 78:(Std. Err. adjusted for {res:180} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0028054{col 26}{space 2} .0142864{col 37}{space 1}   -0.20{col 46}{space 3}0.845{col 54}{space 4}-.0309968{col 67}{space 3} .0253861
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0009149{col 26}{space 2} .0070639{col 37}{space 1}    0.13{col 46}{space 3}0.897{col 54}{space 4}-.0130243{col 67}{space 3} .0148542
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0193506{col 26}{space 2}   .00188{col 37}{space 1}   10.29{col 46}{space 3}0.000{col 54}{space 4} .0156407{col 67}{space 3} .0230605
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002207{col 26}{space 2} .0000206{col 37}{space 1}  -10.71{col 46}{space 3}0.000{col 54}{space 4}-.0002613{col 67}{space 3}  -.00018
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0068466{col 26}{space 2} .0120631{col 37}{space 1}   -0.57{col 46}{space 3}0.571{col 54}{space 4}-.0306508{col 67}{space 3} .0169577
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0375344{col 26}{space 2} .0162373{col 37}{space 1}    2.31{col 46}{space 3}0.022{col 54}{space 4} .0054933{col 67}{space 3} .0695755
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0158014{col 26}{space 2} .0063455{col 37}{space 1}    2.49{col 46}{space 3}0.014{col 54}{space 4} .0032797{col 67}{space 3} .0283231
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0842314{col 26}{space 2} .0112928{col 37}{space 1}    7.46{col 46}{space 3}0.000{col 54}{space 4} .0619473{col 67}{space 3} .1065156
{txt}move_out_m~i {c |}{col 14}{res}{space 2}  .046118{col 26}{space 2} .0121571{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4} .0221282{col 67}{space 3} .0701077
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .0958502{col 26}{space 2} .1490384{col 37}{space 1}    0.64{col 46}{space 3}0.521{col 54}{space 4}-.1982481{col 67}{space 3} .3899485
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0911616{col 26}{space 2} .1065084{col 37}{space 1}   -0.86{col 46}{space 3}0.393{col 54}{space 4}-.3013352{col 67}{space 3}  .119012
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0233657{col 26}{space 2} .0922378{col 37}{space 1}   -0.25{col 46}{space 3}0.800{col 54}{space 4} -.205379{col 67}{space 3} .1586476
{txt}{space 9}10  {c |}{col 14}{res}{space 2}  .011382{col 26}{space 2} .1085333{col 37}{space 1}    0.10{col 46}{space 3}0.917{col 54}{space 4}-.2027873{col 67}{space 3} .2255514
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4283353{col 26}{space 2}  .092026{col 37}{space 1}    4.65{col 46}{space 3}0.000{col 54}{space 4}   .24674{col 67}{space 3} .6099306
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .2750733{col 26}{space 2} .0978287{col 37}{space 1}    2.81{col 46}{space 3}0.005{col 54}{space 4} .0820275{col 67}{space 3} .4681191
{txt}{space 9}13  {c |}{col 14}{res}{space 2}  .139034{col 26}{space 2} .1026307{col 37}{space 1}    1.35{col 46}{space 3}0.177{col 54}{space 4}-.0634876{col 67}{space 3} .3415556
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .3144829{col 26}{space 2} .1039365{col 37}{space 1}    3.03{col 46}{space 3}0.003{col 54}{space 4} .1093844{col 67}{space 3} .5195813
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .2518236{col 26}{space 2} .1246196{col 37}{space 1}    2.02{col 46}{space 3}0.045{col 54}{space 4} .0059112{col 67}{space 3} .4977361
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .4176506{col 26}{space 2} .0387083{col 37}{space 1}   10.79{col 46}{space 3}0.000{col 54}{space 4} .3412673{col 67}{space 3} .4940338
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3839054{col 26}{space 2} .0311306{col 37}{space 1}   12.33{col 46}{space 3}0.000{col 54}{space 4} .3224752{col 67}{space 3} .4453356
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .3825973{col 26}{space 2} .0267613{col 37}{space 1}   14.30{col 46}{space 3}0.000{col 54}{space 4} .3297891{col 67}{space 3} .4354056
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2612178{col 26}{space 2} .0491756{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4} .1641793{col 67}{space 3} .3582562
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .3117108{col 26}{space 2} .0589699{col 37}{space 1}    5.29{col 46}{space 3}0.000{col 54}{space 4} .1953453{col 67}{space 3} .4280763
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .2293234{col 26}{space 2} .0362793{col 37}{space 1}    6.32{col 46}{space 3}0.000{col 54}{space 4} .1577331{col 67}{space 3} .3009136
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .1192537{col 26}{space 2} .0677616{col 37}{space 1}    1.76{col 46}{space 3}0.080{col 54}{space 4}-.0144606{col 67}{space 3}  .252968
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .3315551{col 26}{space 2} .0701953{col 37}{space 1}    4.72{col 46}{space 3}0.000{col 54}{space 4} .1930383{col 67}{space 3} .4700719
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .2990703{col 26}{space 2} .0995072{col 37}{space 1}    3.01{col 46}{space 3}0.003{col 54}{space 4} .1027123{col 67}{space 3} .4954284
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .2570426{col 26}{space 2}  .046485{col 37}{space 1}    5.53{col 46}{space 3}0.000{col 54}{space 4} .1653136{col 67}{space 3} .3487716
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .3263319{col 26}{space 2} .0423454{col 37}{space 1}    7.71{col 46}{space 3}0.000{col 54}{space 4} .2427714{col 67}{space 3} .4098924
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .3068623{col 26}{space 2} .0355336{col 37}{space 1}    8.64{col 46}{space 3}0.000{col 54}{space 4} .2367437{col 67}{space 3}  .376981
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .2190818{col 26}{space 2} .0282856{col 37}{space 1}    7.75{col 46}{space 3}0.000{col 54}{space 4} .1632657{col 67}{space 3} .2748979
{txt}{space 9}29  {c |}{col 14}{res}{space 2}  .134703{col 26}{space 2} .0257566{col 37}{space 1}    5.23{col 46}{space 3}0.000{col 54}{space 4} .0838774{col 67}{space 3} .1855286
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .0850129{col 26}{space 2} .0322917{col 37}{space 1}    2.63{col 46}{space 3}0.009{col 54}{space 4} .0212914{col 67}{space 3} .1487344
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .3249409{col 26}{space 2} .0433689{col 37}{space 1}    7.49{col 46}{space 3}0.000{col 54}{space 4} .2393608{col 67}{space 3}  .410521
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .3195266{col 26}{space 2} .0452929{col 37}{space 1}    7.05{col 46}{space 3}0.000{col 54}{space 4} .2301498{col 67}{space 3} .4089034
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .3076744{col 26}{space 2}   .02663{col 37}{space 1}   11.55{col 46}{space 3}0.000{col 54}{space 4} .2551253{col 67}{space 3} .3602236
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .1720221{col 26}{space 2} .0241216{col 37}{space 1}    7.13{col 46}{space 3}0.000{col 54}{space 4} .1244228{col 67}{space 3} .2196214
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .1760845{col 26}{space 2} .0271132{col 37}{space 1}    6.49{col 46}{space 3}0.000{col 54}{space 4}  .122582{col 67}{space 3} .2295871
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .3205804{col 26}{space 2} .0531473{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4} .2157046{col 67}{space 3} .4254563
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .1363434{col 26}{space 2} .1212535{col 37}{space 1}    1.12{col 46}{space 3}0.262{col 54}{space 4}-.1029269{col 67}{space 3} .3756136
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .3826309{col 26}{space 2} .0463134{col 37}{space 1}    8.26{col 46}{space 3}0.000{col 54}{space 4} .2912404{col 67}{space 3} .4740214
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .2479294{col 26}{space 2}  .074628{col 37}{space 1}    3.32{col 46}{space 3}0.001{col 54}{space 4} .1006655{col 67}{space 3} .3951932
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .2242443{col 26}{space 2} .0453055{col 37}{space 1}    4.95{col 46}{space 3}0.000{col 54}{space 4} .1348428{col 67}{space 3} .3136458
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .2915755{col 26}{space 2} .0268863{col 37}{space 1}   10.84{col 46}{space 3}0.000{col 54}{space 4} .2385206{col 67}{space 3} .3446304
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .2590768{col 26}{space 2} .0233097{col 37}{space 1}   11.11{col 46}{space 3}0.000{col 54}{space 4} .2130796{col 67}{space 3}  .305074
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .2414567{col 26}{space 2} .0236514{col 37}{space 1}   10.21{col 46}{space 3}0.000{col 54}{space 4} .1947852{col 67}{space 3} .2881282
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .1448172{col 26}{space 2} .0474948{col 37}{space 1}    3.05{col 46}{space 3}0.003{col 54}{space 4} .0510955{col 67}{space 3} .2385389
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .1558222{col 26}{space 2} .0476122{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0618689{col 67}{space 3} .2497756
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .4015249{col 26}{space 2} .0585985{col 37}{space 1}    6.85{col 46}{space 3}0.000{col 54}{space 4} .2858921{col 67}{space 3} .5171576
{txt}{space 9}47  {c |}{col 14}{res}{space 2}  .344909{col 26}{space 2} .0373638{col 37}{space 1}    9.23{col 46}{space 3}0.000{col 54}{space 4} .2711788{col 67}{space 3} .4186391
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .2360593{col 26}{space 2} .0370606{col 37}{space 1}    6.37{col 46}{space 3}0.000{col 54}{space 4} .1629275{col 67}{space 3} .3091911
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .1333363{col 26}{space 2} .0390944{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .0561911{col 67}{space 3} .2104814
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .2444462{col 26}{space 2} .0571284{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} .1317144{col 67}{space 3} .3571779
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .4940984{col 26}{space 2} .0613104{col 37}{space 1}    8.06{col 46}{space 3}0.000{col 54}{space 4} .3731143{col 67}{space 3} .6150825
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .3407329{col 26}{space 2} .0802962{col 37}{space 1}    4.24{col 46}{space 3}0.000{col 54}{space 4}  .182284{col 67}{space 3} .4991818
{txt}{space 9}53  {c |}{col 14}{res}{space 2}    .2791{col 26}{space 2} .0426638{col 37}{space 1}    6.54{col 46}{space 3}0.000{col 54}{space 4} .1949113{col 67}{space 3} .3632887
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .2911185{col 26}{space 2} .0720662{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4} .1489098{col 67}{space 3} .4333271
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .4140844{col 26}{space 2} .0634144{col 37}{space 1}    6.53{col 46}{space 3}0.000{col 54}{space 4} .2889483{col 67}{space 3} .5392205
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .0599298{col 26}{space 2} .0317259{col 37}{space 1}    1.89{col 46}{space 3}0.061{col 54}{space 4}-.0026752{col 67}{space 3} .1225348
{txt}{space 9}57  {c |}{col 14}{res}{space 2}-.0124208{col 26}{space 2} .0369996{col 37}{space 1}   -0.34{col 46}{space 3}0.737{col 54}{space 4}-.0854323{col 67}{space 3} .0605906
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .0162077{col 26}{space 2} .0389968{col 37}{space 1}    0.42{col 46}{space 3}0.678{col 54}{space 4}-.0607449{col 67}{space 3} .0931604
{txt}{space 9}59  {c |}{col 14}{res}{space 2}-.0584494{col 26}{space 2} .0388768{col 37}{space 1}   -1.50{col 46}{space 3}0.134{col 54}{space 4}-.1351652{col 67}{space 3} .0182663
{txt}{space 9}60  {c |}{col 14}{res}{space 2}-.0278393{col 26}{space 2} .0356305{col 37}{space 1}   -0.78{col 46}{space 3}0.436{col 54}{space 4}-.0981491{col 67}{space 3} .0424705
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .2414196{col 26}{space 2} .0617919{col 37}{space 1}    3.91{col 46}{space 3}0.000{col 54}{space 4} .1194854{col 67}{space 3} .3633538
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .1777795{col 26}{space 2} .0415892{col 37}{space 1}    4.27{col 46}{space 3}0.000{col 54}{space 4} .0957113{col 67}{space 3} .2598477
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .2003317{col 26}{space 2} .0553188{col 37}{space 1}    3.62{col 46}{space 3}0.000{col 54}{space 4} .0911707{col 67}{space 3} .3094927
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .1518905{col 26}{space 2} .0352765{col 37}{space 1}    4.31{col 46}{space 3}0.000{col 54}{space 4} .0822792{col 67}{space 3} .2215018
{txt}{space 9}65  {c |}{col 14}{res}{space 2}  .032834{col 26}{space 2}  .054517{col 37}{space 1}    0.60{col 46}{space 3}0.548{col 54}{space 4}-.0747447{col 67}{space 3} .1404126
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .4508733{col 26}{space 2} .0862352{col 37}{space 1}    5.23{col 46}{space 3}0.000{col 54}{space 4}  .280705{col 67}{space 3} .6210416
{txt}{space 9}67  {c |}{col 14}{res}{space 2}   .25346{col 26}{space 2} .1090119{col 37}{space 1}    2.33{col 46}{space 3}0.021{col 54}{space 4} .0383463{col 67}{space 3} .4685738
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .2905164{col 26}{space 2} .0512773{col 37}{space 1}    5.67{col 46}{space 3}0.000{col 54}{space 4} .1893307{col 67}{space 3} .3917021
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .2880991{col 26}{space 2} .0428707{col 37}{space 1}    6.72{col 46}{space 3}0.000{col 54}{space 4} .2035021{col 67}{space 3} .3726962
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .2092751{col 26}{space 2} .0636838{col 37}{space 1}    3.29{col 46}{space 3}0.001{col 54}{space 4} .0836076{col 67}{space 3} .3349426
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1605538{col 26}{space 2} .0455738{col 37}{space 1}   -3.52{col 46}{space 3}0.001{col 54}{space 4}-.2504849{col 67}{space 3}-.0706227
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov move_out_muni i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,301
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       749
{txt}{col 49}F({res}  78{txt},{res}    253{txt}){col 67}= {res}    136.64
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1522
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1038
{txt}{col 49}Root MSE{col 67}= {res}    0.4732

{txt}{ralign 78:(Std. Err. adjusted for {res:254} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0225852{col 26}{space 2}  .004875{col 37}{space 1}    4.63{col 46}{space 3}0.000{col 54}{space 4} .0129845{col 67}{space 3}  .032186
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0098634{col 26}{space 2} .0067581{col 37}{space 1}    1.46{col 46}{space 3}0.146{col 54}{space 4} -.003446{col 67}{space 3} .0231727
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0154827{col 26}{space 2} .0016549{col 37}{space 1}    9.36{col 46}{space 3}0.000{col 54}{space 4} .0122235{col 67}{space 3} .0187419
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001789{col 26}{space 2} .0000186{col 37}{space 1}   -9.63{col 46}{space 3}0.000{col 54}{space 4}-.0002155{col 67}{space 3}-.0001423
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0080231{col 26}{space 2} .0093503{col 37}{space 1}    0.86{col 46}{space 3}0.392{col 54}{space 4}-.0103912{col 67}{space 3} .0264374
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0846486{col 26}{space 2} .0192565{col 37}{space 1}    4.40{col 46}{space 3}0.000{col 54}{space 4} .0467251{col 67}{space 3}  .122572
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0128075{col 26}{space 2} .0080241{col 37}{space 1}    1.60{col 46}{space 3}0.112{col 54}{space 4}-.0029951{col 67}{space 3} .0286101
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0781326{col 26}{space 2} .0101624{col 37}{space 1}    7.69{col 46}{space 3}0.000{col 54}{space 4} .0581188{col 67}{space 3} .0981463
{txt}move_out_m~i {c |}{col 14}{res}{space 2} .0614637{col 26}{space 2} .0170795{col 37}{space 1}    3.60{col 46}{space 3}0.000{col 54}{space 4} .0278276{col 67}{space 3} .0950998
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .3930409{col 26}{space 2} .3174576{col 37}{space 1}    1.24{col 46}{space 3}0.217{col 54}{space 4}-.2321552{col 67}{space 3} 1.018237
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4014863{col 26}{space 2} .2235019{col 37}{space 1}    1.80{col 46}{space 3}0.074{col 54}{space 4} -.038675{col 67}{space 3} .8416475
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4797561{col 26}{space 2} .2571856{col 37}{space 1}    1.87{col 46}{space 3}0.063{col 54}{space 4}-.0267414{col 67}{space 3} .9862535
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{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5173847{col 26}{space 2} .0775806{col 37}{space 1}    6.67{col 46}{space 3}0.000{col 54}{space 4} .3645986{col 67}{space 3} .6701708
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .4690917{col 26}{space 2} .0759361{col 37}{space 1}    6.18{col 46}{space 3}0.000{col 54}{space 4} .3195443{col 67}{space 3}  .618639
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{txt}{space 9}67  {c |}{col 14}{res}{space 2} .5195397{col 26}{space 2} .0873421{col 37}{space 1}    5.95{col 46}{space 3}0.000{col 54}{space 4} .3475294{col 67}{space 3}   .69155
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{txt}{space 9}69  {c |}{col 14}{res}{space 2} .4675271{col 26}{space 2} .0849935{col 37}{space 1}    5.50{col 46}{space 3}0.000{col 54}{space 4} .3001422{col 67}{space 3}  .634912
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4511095{col 26}{space 2} .0876797{col 37}{space 1}    5.14{col 46}{space 3}0.000{col 54}{space 4} .2784344{col 67}{space 3} .6237847
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3307824{col 26}{space 2} .0781588{col 37}{space 1}   -4.23{col 46}{space 3}0.000{col 54}{space 4}-.4847072{col 67}{space 3}-.1768577
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_move.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_move.tex"'})

{com}. 
. 
.  ///LONG TERM RESULTS
>  
.  preserve
{txt}
{com}.  ///long term: MERGE ON 2010 VOTING DATA
>  
. rename r r_first
{res}{txt}
{com}. rename k k_first
{res}{txt}
{com}. rename l l_first
{res}{txt}
{com}.  
.  
. merge 1:m LopNr using "D:\SCB_ConPol\Stata\VD\Valdelt_2010", keepusing (LopNr r k l)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,213,376
{txt}{col 9}from master{col 30}{res}          11,355{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,202,021{txt}  (_merge==2)

{col 5}matched{col 30}{res}          52,018{txt}  (_merge==3)
{col 5}{hline 41}

{com}. keep if _merge==3
{txt}(7,213,376 observations deleted)

{com}. 
. 
. *check for duplicates - OK
. *duplicates report LopNr 
. 
. ///create voting variable
> 
. ///voted or not
> gen voted_k2010 =0 if k==1
{txt}(36,940 missing values generated)

{com}. replace voted_k2010=1 if voted_k2010==.
{txt}(36,940 real changes made)

{com}. 
. label variable voted_k2010  "voted in local election 2010"
{txt}
{com}. 
. ///Baseline reg
> eststo clear
{txt}
{com}. 
. eststo: areg voted_k2010 stdcoshare $cov move_out_muni i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    49,990
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}     1,006
{txt}{col 49}F({res}  78{txt},{res}    284{txt}){col 67}= {res}     66.01
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0697
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0491
{txt}{col 49}Root MSE{col 67}= {res}    0.4414

{txt}{ralign 78:(Std. Err. adjusted for {res:285} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0019666{col 26}{space 2} .0035525{col 37}{space 1}   -0.55{col 46}{space 3}0.580{col 54}{space 4}-.0089592{col 67}{space 3} .0050259
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0276547{col 26}{space 2} .0055361{col 37}{space 1}   -5.00{col 46}{space 3}0.000{col 54}{space 4}-.0385517{col 67}{space 3}-.0167578
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0146417{col 26}{space 2} .0011455{col 37}{space 1}   12.78{col 46}{space 3}0.000{col 54}{space 4} .0123868{col 67}{space 3} .0168965
{txt}{space 8}age2 {c |}{col 14}{res}{space 2} -.000246{col 26}{space 2} .0000138{col 37}{space 1}  -17.88{col 46}{space 3}0.000{col 54}{space 4}-.0002731{col 67}{space 3}-.0002189
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0142672{col 26}{space 2} .0052747{col 37}{space 1}    2.70{col 46}{space 3}0.007{col 54}{space 4} .0038846{col 67}{space 3} .0246498
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{txt}{space 4}children {c |}{col 14}{res}{space 2} .0015725{col 26}{space 2} .0023075{col 37}{space 1}    0.68{col 46}{space 3}0.496{col 54}{space 4}-.0029695{col 67}{space 3} .0061145
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0520752{col 26}{space 2} .0056513{col 37}{space 1}    9.21{col 46}{space 3}0.000{col 54}{space 4} .0409513{col 67}{space 3}  .063199
{txt}move_out_m~i {c |}{col 14}{res}{space 2} .0160325{col 26}{space 2} .0063254{col 37}{space 1}    2.53{col 46}{space 3}0.012{col 54}{space 4} .0035819{col 67}{space 3} .0284831
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2}  .703043{col 26}{space 2} .1430927{col 37}{space 1}    4.91{col 46}{space 3}0.000{col 54}{space 4} .4213861{col 67}{space 3} .9846999
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{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2320688{col 26}{space 2}  .292252{col 37}{space 1}    0.79{col 46}{space 3}0.428{col 54}{space 4}-.3431861{col 67}{space 3} .8073236
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0305533{col 26}{space 2}  .209343{col 37}{space 1}    0.15{col 46}{space 3}0.884{col 54}{space 4}-.3815075{col 67}{space 3}  .442614
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1777058{col 26}{space 2} .1480558{col 37}{space 1}    1.20{col 46}{space 3}0.231{col 54}{space 4}-.1137203{col 67}{space 3} .4691318
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1954051{col 26}{space 2} .1446187{col 37}{space 1}    1.35{col 46}{space 3}0.178{col 54}{space 4}-.0892554{col 67}{space 3} .4800657
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2650459{col 26}{space 2} .1502238{col 37}{space 1}    1.76{col 46}{space 3}0.079{col 54}{space 4}-.0306475{col 67}{space 3} .5607393
{txt}{space 10}9  {c |}{col 14}{res}{space 2}  .288364{col 26}{space 2}  .147728{col 37}{space 1}    1.95{col 46}{space 3}0.052{col 54}{space 4}-.0024168{col 67}{space 3} .5791448
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2530035{col 26}{space 2} .1491159{col 37}{space 1}    1.70{col 46}{space 3}0.091{col 54}{space 4}-.0405091{col 67}{space 3}  .546516
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4724516{col 26}{space 2} .1485631{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4}  .180027{col 67}{space 3} .7648761
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{txt}{space 9}68  {c |}{col 14}{res}{space 2} .3991994{col 26}{space 2} .1558068{col 37}{space 1}    2.56{col 46}{space 3}0.011{col 54}{space 4} .0925167{col 67}{space 3} .7058821
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .4944026{col 26}{space 2} .1444079{col 37}{space 1}    3.42{col 46}{space 3}0.001{col 54}{space 4} .2101569{col 67}{space 3} .7786482
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4311863{col 26}{space 2} .1684633{col 37}{space 1}    2.56{col 46}{space 3}0.011{col 54}{space 4} .0995913{col 67}{space 3} .7627814
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .1140016{col 26}{space 2} .1451824{col 37}{space 1}    0.79{col 46}{space 3}0.433{col 54}{space 4}-.1717685{col 67}{space 3} .3997717
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k2010 stdcoshare $cov move_out_muni i.cohortbycountry if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    13,186
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       791
{txt}{col 49}F({res}  74{txt},{res}    277{txt}){col 67}= {res}      8.45
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1016
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0385
{txt}{col 49}Root MSE{col 67}= {res}    0.4349

{txt}{ralign 78:(Std. Err. adjusted for {res:278} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0037157{col 26}{space 2} .0093924{col 37}{space 1}    0.40{col 46}{space 3}0.693{col 54}{space 4}-.0147738{col 67}{space 3} .0222052
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0370789{col 26}{space 2} .0093806{col 37}{space 1}   -3.95{col 46}{space 3}0.000{col 54}{space 4}-.0555452{col 67}{space 3}-.0186126
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0173758{col 26}{space 2} .0025405{col 37}{space 1}    6.84{col 46}{space 3}0.000{col 54}{space 4} .0123747{col 67}{space 3} .0223769
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002781{col 26}{space 2}  .000034{col 37}{space 1}   -8.18{col 46}{space 3}0.000{col 54}{space 4}-.0003451{col 67}{space 3}-.0002112
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0127348{col 26}{space 2} .0120654{col 37}{space 1}    1.06{col 46}{space 3}0.292{col 54}{space 4}-.0110168{col 67}{space 3} .0364863
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0269842{col 26}{space 2} .0153696{col 37}{space 1}    1.76{col 46}{space 3}0.080{col 54}{space 4}-.0032719{col 67}{space 3} .0572403
{txt}{space 4}children {c |}{col 14}{res}{space 2}-.0044661{col 26}{space 2} .0047262{col 37}{space 1}   -0.94{col 46}{space 3}0.346{col 54}{space 4}-.0137698{col 67}{space 3} .0048377
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0276692{col 26}{space 2} .0102955{col 37}{space 1}    2.69{col 46}{space 3}0.008{col 54}{space 4}  .007402{col 67}{space 3} .0479365
{txt}move_out_m~i {c |}{col 14}{res}{space 2} .0041892{col 26}{space 2} .0121153{col 37}{space 1}    0.35{col 46}{space 3}0.730{col 54}{space 4}-.0196605{col 67}{space 3} .0280389
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.7660765{col 26}{space 2}  .196015{col 37}{space 1}   -3.91{col 46}{space 3}0.000{col 54}{space 4}-1.151945{col 67}{space 3}-.3802082
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.6256661{col 26}{space 2}   .34204{col 37}{space 1}   -1.83{col 46}{space 3}0.068{col 54}{space 4}-1.298994{col 67}{space 3} .0476619
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2}-.4632742{col 26}{space 2} .1620215{col 37}{space 1}   -2.86{col 46}{space 3}0.005{col 54}{space 4} -.782224{col 67}{space 3}-.1443243
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.4219699{col 26}{space 2}  .116751{col 37}{space 1}   -3.61{col 46}{space 3}0.000{col 54}{space 4}-.6518019{col 67}{space 3} -.192138
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.5569267{col 26}{space 2} .1251755{col 37}{space 1}   -4.45{col 46}{space 3}0.000{col 54}{space 4}-.8033429{col 67}{space 3}-.3105105
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.2644789{col 26}{space 2} .0705861{col 37}{space 1}   -3.75{col 46}{space 3}0.000{col 54}{space 4}-.4034322{col 67}{space 3}-.1255256
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.3243585{col 26}{space 2} .0700234{col 37}{space 1}   -4.63{col 46}{space 3}0.000{col 54}{space 4}-.4622041{col 67}{space 3}-.1865129
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.3018095{col 26}{space 2} .0786595{col 37}{space 1}   -3.84{col 46}{space 3}0.000{col 54}{space 4}-.4566559{col 67}{space 3}-.1469632
{txt}{space 9}14  {c |}{col 14}{res}{space 2}-.3613412{col 26}{space 2} .0703097{col 37}{space 1}   -5.14{col 46}{space 3}0.000{col 54}{space 4}-.4997504{col 67}{space 3} -.222932
{txt}{space 9}15  {c |}{col 14}{res}{space 2} -.289289{col 26}{space 2} .0789683{col 37}{space 1}   -3.66{col 46}{space 3}0.000{col 54}{space 4}-.4447434{col 67}{space 3}-.1338347
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.3404637{col 26}{space 2} .0596743{col 37}{space 1}   -5.71{col 46}{space 3}0.000{col 54}{space 4}-.4579365{col 67}{space 3}-.2229909
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-.3371491{col 26}{space 2} .0447281{col 37}{space 1}   -7.54{col 46}{space 3}0.000{col 54}{space 4}-.4251992{col 67}{space 3}-.2490989
{txt}{space 9}18  {c |}{col 14}{res}{space 2}-.3934942{col 26}{space 2} .0451506{col 37}{space 1}   -8.72{col 46}{space 3}0.000{col 54}{space 4}-.4823761{col 67}{space 3}-.3046123
{txt}{space 9}19  {c |}{col 14}{res}{space 2}-.3927721{col 26}{space 2} .0665215{col 37}{space 1}   -5.90{col 46}{space 3}0.000{col 54}{space 4}-.5237239{col 67}{space 3}-.2618202
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.4665205{col 26}{space 2} .1683364{col 37}{space 1}   -2.77{col 46}{space 3}0.006{col 54}{space 4}-.7979015{col 67}{space 3}-.1351394
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.4631065{col 26}{space 2} .1804033{col 37}{space 1}   -2.57{col 46}{space 3}0.011{col 54}{space 4}-.8182422{col 67}{space 3}-.1079708
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.2097152{col 26}{space 2} .0968613{col 37}{space 1}   -2.17{col 46}{space 3}0.031{col 54}{space 4}-.4003929{col 67}{space 3}-.0190374
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.3277559{col 26}{space 2} .0916046{col 37}{space 1}   -3.58{col 46}{space 3}0.000{col 54}{space 4}-.5080855{col 67}{space 3}-.1474262
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.2305481{col 26}{space 2} .0664536{col 37}{space 1}   -3.47{col 46}{space 3}0.001{col 54}{space 4}-.3613664{col 67}{space 3}-.0997298
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-.3259862{col 26}{space 2} .1094737{col 37}{space 1}   -2.98{col 46}{space 3}0.003{col 54}{space 4}-.5414923{col 67}{space 3}-.1104801
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.3665144{col 26}{space 2} .0563066{col 37}{space 1}   -6.51{col 46}{space 3}0.000{col 54}{space 4}-.4773575{col 67}{space 3}-.2556712
{txt}{space 9}27  {c |}{col 14}{res}{space 2}-.2688219{col 26}{space 2} .0479612{col 37}{space 1}   -5.60{col 46}{space 3}0.000{col 54}{space 4}-.3632366{col 67}{space 3}-.1744071
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.3340497{col 26}{space 2} .0528941{col 37}{space 1}   -6.32{col 46}{space 3}0.000{col 54}{space 4}-.4381751{col 67}{space 3}-.2299243
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.3207667{col 26}{space 2} .0494528{col 37}{space 1}   -6.49{col 46}{space 3}0.000{col 54}{space 4}-.4181178{col 67}{space 3}-.2234156
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .8396022{col 26}{space 2} .0632663{col 37}{space 1}   13.27{col 46}{space 3}0.000{col 54}{space 4} .7150584{col 67}{space 3}  .964146
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k2010 stdcoshare $cov move_out_muni i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    12,861
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       729
{txt}{col 49}{help j_robustsingular##|_new:F(  73,    275)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1045
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0448
{txt}{col 49}Root MSE{col 67}= {res}    0.4359

{txt}{ralign 78:(Std. Err. adjusted for {res:276} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0115717{col 26}{space 2} .0115643{col 37}{space 1}    1.00{col 46}{space 3}0.318{col 54}{space 4} -.011194{col 67}{space 3} .0343375
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0342972{col 26}{space 2} .0086379{col 37}{space 1}   -3.97{col 46}{space 3}0.000{col 54}{space 4} -.051302{col 67}{space 3}-.0172923
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .014321{col 26}{space 2} .0022166{col 37}{space 1}    6.46{col 46}{space 3}0.000{col 54}{space 4} .0099574{col 67}{space 3} .0186847
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002378{col 26}{space 2} .0000294{col 37}{space 1}   -8.10{col 46}{space 3}0.000{col 54}{space 4}-.0002956{col 67}{space 3}-.0001801
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0161614{col 26}{space 2} .0129018{col 37}{space 1}    1.25{col 46}{space 3}0.211{col 54}{space 4}-.0092374{col 67}{space 3} .0415602
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0080386{col 26}{space 2} .0144661{col 37}{space 1}    0.56{col 46}{space 3}0.579{col 54}{space 4}-.0204398{col 67}{space 3}  .036517
{txt}{space 4}children {c |}{col 14}{res}{space 2}-.0027898{col 26}{space 2}   .00497{col 37}{space 1}   -0.56{col 46}{space 3}0.575{col 54}{space 4}-.0125739{col 67}{space 3} .0069942
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0464295{col 26}{space 2} .0102841{col 37}{space 1}    4.51{col 46}{space 3}0.000{col 54}{space 4} .0261839{col 67}{space 3} .0666751
{txt}move_out_m~i {c |}{col 14}{res}{space 2} .0102104{col 26}{space 2} .0109837{col 37}{space 1}    0.93{col 46}{space 3}0.353{col 54}{space 4}-.0114125{col 67}{space 3} .0318333
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}39  {c |}{col 14}{res}{space 2} .8940339{col 26}{space 2} .0997576{col 37}{space 1}    8.96{col 46}{space 3}0.000{col 54}{space 4} .6976483{col 67}{space 3}  1.09042
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1745176{col 26}{space 2} .0508636{col 37}{space 1}   -3.43{col 46}{space 3}0.001{col 54}{space 4} -.274649{col 67}{space 3}-.0743862
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k2010 stdcoshare $cov move_out_muni i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    12,387
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       401
{txt}{col 49}F({res}  73{txt},{res}    173{txt}){col 67}= {res}     59.74
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.0926
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0566
{txt}{col 49}Root MSE{col 67}= {res}    0.4431

{txt}{ralign 78:(Std. Err. adjusted for {res:174} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0062988{col 26}{space 2} .0118631{col 37}{space 1}    0.53{col 46}{space 3}0.596{col 54}{space 4}-.0171163{col 67}{space 3} .0297138
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0360753{col 26}{space 2} .0086402{col 37}{space 1}   -4.18{col 46}{space 3}0.000{col 54}{space 4}-.0531289{col 67}{space 3}-.0190216
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0184839{col 26}{space 2} .0025346{col 37}{space 1}    7.29{col 46}{space 3}0.000{col 54}{space 4} .0134812{col 67}{space 3} .0234866
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002895{col 26}{space 2} .0000311{col 37}{space 1}   -9.30{col 46}{space 3}0.000{col 54}{space 4} -.000351{col 67}{space 3}-.0002281
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0309791{col 26}{space 2} .0105679{col 37}{space 1}    2.93{col 46}{space 3}0.004{col 54}{space 4} .0101205{col 67}{space 3} .0518377
{txt}{space 6}parent {c |}{col 14}{res}{space 2}-.0005791{col 26}{space 2} .0153152{col 37}{space 1}   -0.04{col 46}{space 3}0.970{col 54}{space 4}-.0308078{col 67}{space 3} .0296495
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0011481{col 26}{space 2} .0058928{col 37}{space 1}    0.19{col 46}{space 3}0.846{col 54}{space 4}-.0104829{col 67}{space 3} .0127791
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0531155{col 26}{space 2} .0106264{col 37}{space 1}    5.00{col 46}{space 3}0.000{col 54}{space 4} .0321414{col 67}{space 3} .0740897
{txt}move_out_m~i {c |}{col 14}{res}{space 2} .0295184{col 26}{space 2} .0113063{col 37}{space 1}    2.61{col 46}{space 3}0.010{col 54}{space 4} .0072024{col 67}{space 3} .0518344
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}67  {c |}{col 14}{res}{space 2} .3939506{col 26}{space 2} .2026889{col 37}{space 1}    1.94{col 46}{space 3}0.054{col 54}{space 4} -.006111{col 67}{space 3} .7940122
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .3157589{col 26}{space 2} .2075803{col 37}{space 1}    1.52{col 46}{space 3}0.130{col 54}{space 4} -.093957{col 67}{space 3} .7254749
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .4513496{col 26}{space 2} .1914682{col 37}{space 1}    2.36{col 46}{space 3}0.020{col 54}{space 4} .0734352{col 67}{space 3} .8292641
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4586163{col 26}{space 2} .2031302{col 37}{space 1}    2.26{col 46}{space 3}0.025{col 54}{space 4} .0576837{col 67}{space 3} .8595489
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .0560597{col 26}{space 2} .2179372{col 37}{space 1}    0.26{col 46}{space 3}0.797{col 54}{space 4}-.3740985{col 67}{space 3} .4862178
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k2010 stdcoshare $cov move_out_muni i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    11,556
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       681
{txt}{col 49}F({res}  78{txt},{res}    242{txt}){col 67}= {res}    141.65
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1391
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0787
{txt}{col 49}Root MSE{col 67}= {res}    0.4461

{txt}{ralign 78:(Std. Err. adjusted for {res:243} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} voted_k2010{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0015828{col 26}{space 2} .0045523{col 37}{space 1}   -0.35{col 46}{space 3}0.728{col 54}{space 4}-.0105499{col 67}{space 3} .0073843
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0021758{col 26}{space 2} .0132896{col 37}{space 1}   -0.16{col 46}{space 3}0.870{col 54}{space 4}-.0283538{col 67}{space 3} .0240021
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0067835{col 26}{space 2} .0016765{col 37}{space 1}    4.05{col 46}{space 3}0.000{col 54}{space 4} .0034812{col 67}{space 3} .0100859
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001563{col 26}{space 2} .0000218{col 37}{space 1}   -7.16{col 46}{space 3}0.000{col 54}{space 4}-.0001993{col 67}{space 3}-.0001133
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0068197{col 26}{space 2} .0110557{col 37}{space 1}    0.62{col 46}{space 3}0.538{col 54}{space 4}-.0149579{col 67}{space 3} .0285973
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0310722{col 26}{space 2} .0148965{col 37}{space 1}    2.09{col 46}{space 3}0.038{col 54}{space 4} .0017289{col 67}{space 3} .0604155
{txt}{space 4}children {c |}{col 14}{res}{space 2}  .004025{col 26}{space 2} .0060121{col 37}{space 1}    0.67{col 46}{space 3}0.504{col 54}{space 4}-.0078178{col 67}{space 3} .0158677
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0733459{col 26}{space 2} .0094277{col 37}{space 1}    7.78{col 46}{space 3}0.000{col 54}{space 4} .0547751{col 67}{space 3} .0919168
{txt}move_out_m~i {c |}{col 14}{res}{space 2} .0175468{col 26}{space 2} .0179147{col 37}{space 1}    0.98{col 46}{space 3}0.328{col 54}{space 4}-.0177418{col 67}{space 3} .0528354
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .5219973{col 26}{space 2} .1086326{col 37}{space 1}    4.81{col 46}{space 3}0.000{col 54}{space 4} .3080111{col 67}{space 3} .7359835
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1498588{col 26}{space 2} .1743565{col 37}{space 1}    0.86{col 46}{space 3}0.391{col 54}{space 4}-.1935913{col 67}{space 3} .4933089
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0228807{col 26}{space 2} .2383943{col 37}{space 1}    0.10{col 46}{space 3}0.924{col 54}{space 4}-.4467121{col 67}{space 3} .4924734
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1833071{col 26}{space 2} .1768649{col 37}{space 1}   -1.04{col 46}{space 3}0.301{col 54}{space 4}-.5316983{col 67}{space 3} .1650842
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0355835{col 26}{space 2} .0886061{col 37}{space 1}   -0.40{col 46}{space 3}0.688{col 54}{space 4}-.2101211{col 67}{space 3} .1389541
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0131817{col 26}{space 2} .0978921{col 37}{space 1}   -0.13{col 46}{space 3}0.893{col 54}{space 4}-.2060111{col 67}{space 3} .1796477
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0634323{col 26}{space 2} .0946495{col 37}{space 1}    0.67{col 46}{space 3}0.503{col 54}{space 4}-.1230097{col 67}{space 3} .2498743
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0625889{col 26}{space 2} .0996099{col 37}{space 1}    0.63{col 46}{space 3}0.530{col 54}{space 4}-.1336242{col 67}{space 3} .2588021
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0189302{col 26}{space 2} .1018227{col 37}{space 1}    0.19{col 46}{space 3}0.853{col 54}{space 4}-.1816418{col 67}{space 3} .2195021
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .1579593{col 26}{space 2} .1394118{col 37}{space 1}    1.13{col 46}{space 3}0.258{col 54}{space 4}-.1166561{col 67}{space 3} .4325748
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .1325668{col 26}{space 2} .1395047{col 37}{space 1}    0.95{col 46}{space 3}0.343{col 54}{space 4}-.1422316{col 67}{space 3} .4073652
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .1653278{col 26}{space 2}  .106563{col 37}{space 1}    1.55{col 46}{space 3}0.122{col 54}{space 4}-.0445816{col 67}{space 3} .3752372
{txt}{space 9}14  {c |}{col 14}{res}{space 2}  .137276{col 26}{space 2} .1112057{col 37}{space 1}    1.23{col 46}{space 3}0.218{col 54}{space 4}-.0817787{col 67}{space 3} .3563306
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .3341965{col 26}{space 2} .1104854{col 37}{space 1}    3.02{col 46}{space 3}0.003{col 54}{space 4} .1165608{col 67}{space 3} .5518323
{txt}{space 9}16  {c |}{col 14}{res}{space 2}  .231079{col 26}{space 2} .0969359{col 37}{space 1}    2.38{col 46}{space 3}0.018{col 54}{space 4} .0401332{col 67}{space 3} .4220249
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .2597619{col 26}{space 2} .0969923{col 37}{space 1}    2.68{col 46}{space 3}0.008{col 54}{space 4}  .068705{col 67}{space 3} .4508189
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .2188333{col 26}{space 2}  .095529{col 37}{space 1}    2.29{col 46}{space 3}0.023{col 54}{space 4} .0306588{col 67}{space 3} .4070077
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2214983{col 26}{space 2} .1147852{col 37}{space 1}    1.93{col 46}{space 3}0.055{col 54}{space 4}-.0046074{col 67}{space 3}  .447604
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .1822534{col 26}{space 2} .1142554{col 37}{space 1}    1.60{col 46}{space 3}0.112{col 54}{space 4}-.0428085{col 67}{space 3} .4073154
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .2538987{col 26}{space 2} .0937923{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .0691453{col 67}{space 3} .4386522
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .3360149{col 26}{space 2} .1055193{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4} .1281615{col 67}{space 3} .5438684
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .2697303{col 26}{space 2} .0990341{col 37}{space 1}    2.72{col 46}{space 3}0.007{col 54}{space 4} .0746515{col 67}{space 3} .4648091
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .2676179{col 26}{space 2} .0913573{col 37}{space 1}    2.93{col 46}{space 3}0.004{col 54}{space 4}  .087661{col 67}{space 3} .4475749
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .2568254{col 26}{space 2} .0888891{col 37}{space 1}    2.89{col 46}{space 3}0.004{col 54}{space 4} .0817303{col 67}{space 3} .4319205
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .2156043{col 26}{space 2}  .086657{col 37}{space 1}    2.49{col 46}{space 3}0.014{col 54}{space 4} .0449061{col 67}{space 3} .3863025
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .1756787{col 26}{space 2} .1194411{col 37}{space 1}    1.47{col 46}{space 3}0.143{col 54}{space 4}-.0595981{col 67}{space 3} .4109555
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .2039832{col 26}{space 2} .1319585{col 37}{space 1}    1.55{col 46}{space 3}0.123{col 54}{space 4}-.0559506{col 67}{space 3}  .463917
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .2285065{col 26}{space 2} .1376911{col 37}{space 1}    1.66{col 46}{space 3}0.098{col 54}{space 4}-.0427196{col 67}{space 3} .4997326
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .1894101{col 26}{space 2}  .142302{col 37}{space 1}    1.33{col 46}{space 3}0.184{col 54}{space 4}-.0908986{col 67}{space 3} .4697188
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .1351605{col 26}{space 2}  .088246{col 37}{space 1}    1.53{col 46}{space 3}0.127{col 54}{space 4}-.0386677{col 67}{space 3} .3089887
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .1258839{col 26}{space 2}   .09723{col 37}{space 1}    1.29{col 46}{space 3}0.197{col 54}{space 4}-.0656413{col 67}{space 3} .3174091
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .1488346{col 26}{space 2} .1009323{col 37}{space 1}    1.47{col 46}{space 3}0.142{col 54}{space 4}-.0499833{col 67}{space 3} .3476524
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .1007112{col 26}{space 2} .0950303{col 37}{space 1}    1.06{col 46}{space 3}0.290{col 54}{space 4}-.0864809{col 67}{space 3} .2879034
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .1714824{col 26}{space 2} .0991989{col 37}{space 1}    1.73{col 46}{space 3}0.085{col 54}{space 4}-.0239211{col 67}{space 3} .3668859
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .1264739{col 26}{space 2} .0928925{col 37}{space 1}    1.36{col 46}{space 3}0.175{col 54}{space 4}-.0565073{col 67}{space 3}  .309455
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .1594183{col 26}{space 2}   .09799{col 37}{space 1}    1.63{col 46}{space 3}0.105{col 54}{space 4}-.0336039{col 67}{space 3} .3524406
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .2141803{col 26}{space 2} .0972041{col 37}{space 1}    2.20{col 46}{space 3}0.029{col 54}{space 4} .0227061{col 67}{space 3} .4056544
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .1145999{col 26}{space 2} .1028233{col 37}{space 1}    1.11{col 46}{space 3}0.266{col 54}{space 4}-.0879431{col 67}{space 3} .3171429
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .1723122{col 26}{space 2} .1233607{col 37}{space 1}    1.40{col 46}{space 3}0.164{col 54}{space 4}-.0706855{col 67}{space 3}   .41531
{txt}{space 9}41  {c |}{col 14}{res}{space 2}  .202453{col 26}{space 2} .1121645{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4}-.0184903{col 67}{space 3} .4233964
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .2594697{col 26}{space 2} .1290132{col 37}{space 1}    2.01{col 46}{space 3}0.045{col 54}{space 4} .0053374{col 67}{space 3} .5136019
{txt}{space 9}43  {c |}{col 14}{res}{space 2}   .44974{col 26}{space 2} .1422262{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4} .1695806{col 67}{space 3} .7298993
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .0412957{col 26}{space 2} .1532225{col 37}{space 1}    0.27{col 46}{space 3}0.788{col 54}{space 4}-.2605242{col 67}{space 3} .3431157
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .3118549{col 26}{space 2} .2407169{col 37}{space 1}    1.30{col 46}{space 3}0.196{col 54}{space 4} -.162313{col 67}{space 3} .7860228
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .0329681{col 26}{space 2} .1871192{col 37}{space 1}    0.18{col 46}{space 3}0.860{col 54}{space 4}-.3356222{col 67}{space 3} .4015584
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .4017417{col 26}{space 2} .1128532{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .1794419{col 67}{space 3} .6240416
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .2737352{col 26}{space 2} .1292586{col 37}{space 1}    2.12{col 46}{space 3}0.035{col 54}{space 4} .0191196{col 67}{space 3} .5283508
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .1641265{col 26}{space 2} .1445408{col 37}{space 1}    1.14{col 46}{space 3}0.257{col 54}{space 4}-.1205921{col 67}{space 3} .4488451
{txt}{space 9}50  {c |}{col 14}{res}{space 2}-.0152419{col 26}{space 2} .1594829{col 37}{space 1}   -0.10{col 46}{space 3}0.924{col 54}{space 4}-.3293937{col 67}{space 3} .2989099
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .1986217{col 26}{space 2} .1125979{col 37}{space 1}    1.76{col 46}{space 3}0.079{col 54}{space 4}-.0231754{col 67}{space 3} .4204188
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .1293629{col 26}{space 2} .0915771{col 37}{space 1}    1.41{col 46}{space 3}0.159{col 54}{space 4}-.0510271{col 67}{space 3} .3097528
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .2087477{col 26}{space 2} .0845758{col 37}{space 1}    2.47{col 46}{space 3}0.014{col 54}{space 4}  .042149{col 67}{space 3} .3753464
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .1949256{col 26}{space 2} .0920537{col 37}{space 1}    2.12{col 46}{space 3}0.035{col 54}{space 4} .0135967{col 67}{space 3} .3762544
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .2088377{col 26}{space 2} .0915993{col 37}{space 1}    2.28{col 46}{space 3}0.023{col 54}{space 4}  .028404{col 67}{space 3} .3892714
{txt}{space 9}56  {c |}{col 14}{res}{space 2}-.0367432{col 26}{space 2} .1341555{col 37}{space 1}   -0.27{col 46}{space 3}0.784{col 54}{space 4}-.3010048{col 67}{space 3} .2275183
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .1656413{col 26}{space 2} .1538899{col 37}{space 1}    1.08{col 46}{space 3}0.283{col 54}{space 4}-.1374933{col 67}{space 3} .4687759
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .1528539{col 26}{space 2} .1201605{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}  -.08384{col 67}{space 3} .3895479
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .2268237{col 26}{space 2} .1050946{col 37}{space 1}    2.16{col 46}{space 3}0.032{col 54}{space 4} .0198067{col 67}{space 3} .4338407
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .2791419{col 26}{space 2} .1507941{col 37}{space 1}    1.85{col 46}{space 3}0.065{col 54}{space 4}-.0178946{col 67}{space 3} .5761785
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .1285064{col 26}{space 2} .1080639{col 37}{space 1}    1.19{col 46}{space 3}0.236{col 54}{space 4}-.0843595{col 67}{space 3} .3413722
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .1411787{col 26}{space 2} .1164601{col 37}{space 1}    1.21{col 46}{space 3}0.227{col 54}{space 4}-.0882261{col 67}{space 3} .3705835
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .0640248{col 26}{space 2} .1066862{col 37}{space 1}    0.60{col 46}{space 3}0.549{col 54}{space 4}-.1461272{col 67}{space 3} .2741768
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .1199848{col 26}{space 2} .1128893{col 37}{space 1}    1.06{col 46}{space 3}0.289{col 54}{space 4}-.1023862{col 67}{space 3} .3423558
{txt}{space 9}65  {c |}{col 14}{res}{space 2}-.0006646{col 26}{space 2} .1144485{col 37}{space 1}   -0.01{col 46}{space 3}0.995{col 54}{space 4}-.2261071{col 67}{space 3} .2247778
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .2301898{col 26}{space 2}  .088118{col 37}{space 1}    2.61{col 46}{space 3}0.010{col 54}{space 4} .0566135{col 67}{space 3}  .403766
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .2673521{col 26}{space 2}  .110651{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .0493901{col 67}{space 3} .4853142
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .2021643{col 26}{space 2} .0885158{col 37}{space 1}    2.28{col 46}{space 3}0.023{col 54}{space 4} .0278045{col 67}{space 3}  .376524
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .2983436{col 26}{space 2} .0997662{col 37}{space 1}    2.99{col 46}{space 3}0.003{col 54}{space 4} .1018227{col 67}{space 3} .4948646
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .2722623{col 26}{space 2} .1239031{col 37}{space 1}    2.20{col 46}{space 3}0.029{col 54}{space 4} .0281961{col 67}{space 3} .5163285
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .4496006{col 26}{space 2} .0971758{col 37}{space 1}    4.63{col 46}{space 3}0.000{col 54}{space 4} .2581822{col 67}{space 3}  .641019
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reglong_move.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reglong_move.tex"'})

{com}. 
. 
. restore
{txt}
{com}. 
. 
.  //////////////////////////
> ///MORE DIAGNOSTICS!
>  
. /// DO THE SHORT RUN RESULTS USING THE LONG RUN SAMPLE
> 
.  preserve
{txt}
{com}. rename r r_first
{res}{txt}
{com}. rename k k_first
{res}{txt}
{com}. rename l l_first
{res}{txt}
{com}. 
. merge 1:m LopNr using "D:\SCB_ConPol\Stata\VD\Valdelt_2010", keepusing (LopNr r k l)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,213,376
{txt}{col 9}from master{col 30}{res}          11,355{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,202,021{txt}  (_merge==2)

{col 5}matched{col 30}{res}          52,018{txt}  (_merge==3)
{col 5}{hline 41}

{com}. keep if _merge==3
{txt}(7,213,376 observations deleted)

{com}. 
. ///Baseline reg
> eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    49,990
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}     1,006
{txt}{col 49}F({res}  77{txt},{res}    284{txt}){col 67}= {res}     88.66
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1038
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0840
{txt}{col 49}Root MSE{col 67}= {res}    0.4746

{txt}{ralign 78:(Std. Err. adjusted for {res:285} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}   .00293{col 26}{space 2} .0077089{col 37}{space 1}    0.38{col 46}{space 3}0.704{col 54}{space 4}-.0122437{col 67}{space 3} .0181038
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0163988{col 26}{space 2} .0048769{col 37}{space 1}    3.36{col 46}{space 3}0.001{col 54}{space 4} .0067994{col 67}{space 3} .0259983
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0239317{col 26}{space 2} .0016872{col 37}{space 1}   14.18{col 46}{space 3}0.000{col 54}{space 4} .0206107{col 67}{space 3} .0272528
{txt}{space 8}age2 {c |}{col 14}{res}{space 2} -.000277{col 26}{space 2} .0000224{col 37}{space 1}  -12.37{col 46}{space 3}0.000{col 54}{space 4}-.0003211{col 67}{space 3}-.0002329
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0148932{col 26}{space 2} .0068427{col 37}{space 1}    2.18{col 46}{space 3}0.030{col 54}{space 4} .0014243{col 67}{space 3}  .028362
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0314244{col 26}{space 2} .0085672{col 37}{space 1}    3.67{col 46}{space 3}0.000{col 54}{space 4} .0145611{col 67}{space 3} .0482878
{txt}{space 4}children {c |}{col 14}{res}{space 2}   .00956{col 26}{space 2} .0034462{col 37}{space 1}    2.77{col 46}{space 3}0.006{col 54}{space 4} .0027766{col 67}{space 3} .0163434
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0656001{col 26}{space 2} .0058586{col 37}{space 1}   11.20{col 46}{space 3}0.000{col 54}{space 4} .0540682{col 67}{space 3} .0771319
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .3495045{col 26}{space 2} .3039166{col 37}{space 1}    1.15{col 46}{space 3}0.251{col 54}{space 4}-.2487103{col 67}{space 3} .9477194
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2234743{col 26}{space 2} .2057237{col 37}{space 1}    1.09{col 46}{space 3}0.278{col 54}{space 4}-.1814624{col 67}{space 3}  .628411
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2032402{col 26}{space 2} .1298085{col 37}{space 1}    1.57{col 46}{space 3}0.119{col 54}{space 4}-.0522686{col 67}{space 3}  .458749
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1461683{col 26}{space 2} .1967767{col 37}{space 1}    0.74{col 46}{space 3}0.458{col 54}{space 4}-.2411576{col 67}{space 3} .5334942
{txt}{space 10}6  {c |}{col 14}{res}{space 2}  .329015{col 26}{space 2} .1461027{col 37}{space 1}    2.25{col 46}{space 3}0.025{col 54}{space 4} .0414334{col 67}{space 3} .6165965
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .3544604{col 26}{space 2}  .111805{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4} .1343888{col 67}{space 3} .5745321
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2905008{col 26}{space 2} .1264239{col 37}{space 1}    2.30{col 46}{space 3}0.022{col 54}{space 4}  .041654{col 67}{space 3} .5393476
{txt}{space 10}9  {c |}{col 14}{res}{space 2}  .250254{col 26}{space 2} .1260279{col 37}{space 1}    1.99{col 46}{space 3}0.048{col 54}{space 4} .0021866{col 67}{space 3} .4983214
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2517676{col 26}{space 2}  .128997{col 37}{space 1}    1.95{col 46}{space 3}0.052{col 54}{space 4}-.0021439{col 67}{space 3} .5056791
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .6386971{col 26}{space 2} .1240198{col 37}{space 1}    5.15{col 46}{space 3}0.000{col 54}{space 4} .3945825{col 67}{space 3} .8828117
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5516527{col 26}{space 2} .1264115{col 37}{space 1}    4.36{col 46}{space 3}0.000{col 54}{space 4} .3028303{col 67}{space 3} .8004751
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5348547{col 26}{space 2} .1117329{col 37}{space 1}    4.79{col 46}{space 3}0.000{col 54}{space 4} .3149249{col 67}{space 3} .7547844
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5452452{col 26}{space 2} .1241253{col 37}{space 1}    4.39{col 46}{space 3}0.000{col 54}{space 4} .3009228{col 67}{space 3} .7895676
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5383251{col 26}{space 2} .1425096{col 37}{space 1}    3.78{col 46}{space 3}0.000{col 54}{space 4}  .257816{col 67}{space 3} .8188342
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .6180918{col 26}{space 2} .1136819{col 37}{space 1}    5.44{col 46}{space 3}0.000{col 54}{space 4} .3943257{col 67}{space 3} .8418579
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .6146272{col 26}{space 2} .1142621{col 37}{space 1}    5.38{col 46}{space 3}0.000{col 54}{space 4}  .389719{col 67}{space 3} .8395353
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5873483{col 26}{space 2} .1167274{col 37}{space 1}    5.03{col 46}{space 3}0.000{col 54}{space 4} .3575875{col 67}{space 3}  .817109
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .5248101{col 26}{space 2} .1256129{col 37}{space 1}    4.18{col 46}{space 3}0.000{col 54}{space 4} .2775596{col 67}{space 3} .7720606
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .6277085{col 26}{space 2} .1426642{col 37}{space 1}    4.40{col 46}{space 3}0.000{col 54}{space 4} .3468951{col 67}{space 3} .9085218
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .5446202{col 26}{space 2} .1407066{col 37}{space 1}    3.87{col 46}{space 3}0.000{col 54}{space 4}   .26766{col 67}{space 3} .8215804
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .5831392{col 26}{space 2} .1160491{col 37}{space 1}    5.02{col 46}{space 3}0.000{col 54}{space 4} .3547138{col 67}{space 3} .8115646
{txt}{space 9}23  {c |}{col 14}{res}{space 2}  .634638{col 26}{space 2} .1302669{col 37}{space 1}    4.87{col 46}{space 3}0.000{col 54}{space 4}  .378227{col 67}{space 3} .8910491
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .5155927{col 26}{space 2} .1360919{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4} .2477159{col 67}{space 3} .7834694
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5022965{col 26}{space 2}  .117154{col 37}{space 1}    4.29{col 46}{space 3}0.000{col 54}{space 4} .2716962{col 67}{space 3} .7328968
{txt}{space 9}26  {c |}{col 14}{res}{space 2}  .523521{col 26}{space 2} .1116631{col 37}{space 1}    4.69{col 46}{space 3}0.000{col 54}{space 4} .3037287{col 67}{space 3} .7433133
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{txt}{space 9}28  {c |}{col 14}{res}{space 2} .4324262{col 26}{space 2} .1079125{col 37}{space 1}    4.01{col 46}{space 3}0.000{col 54}{space 4} .2200163{col 67}{space 3} .6448361
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .3847839{col 26}{space 2} .1141832{col 37}{space 1}    3.37{col 46}{space 3}0.001{col 54}{space 4} .1600312{col 67}{space 3} .6095366
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{txt}{space 9}37  {c |}{col 14}{res}{space 2} .4568254{col 26}{space 2} .0975133{col 37}{space 1}    4.68{col 46}{space 3}0.000{col 54}{space 4}  .264885{col 67}{space 3} .6487659
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{txt}{space 9}44  {c |}{col 14}{res}{space 2} .4308062{col 26}{space 2} .1227048{col 37}{space 1}    3.51{col 46}{space 3}0.001{col 54}{space 4} .1892799{col 67}{space 3} .6723325
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{txt}{space 9}46  {c |}{col 14}{res}{space 2} .5077228{col 26}{space 2} .1204906{col 37}{space 1}    4.21{col 46}{space 3}0.000{col 54}{space 4} .2705549{col 67}{space 3} .7448906
{txt}{space 9}47  {c |}{col 14}{res}{space 2}  .516774{col 26}{space 2} .1139364{col 37}{space 1}    4.54{col 46}{space 3}0.000{col 54}{space 4}  .292507{col 67}{space 3} .7410411
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .5176682{col 26}{space 2} .1178412{col 37}{space 1}    4.39{col 46}{space 3}0.000{col 54}{space 4} .2857154{col 67}{space 3} .7496211
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .4345565{col 26}{space 2}   .12323{col 37}{space 1}    3.53{col 46}{space 3}0.000{col 54}{space 4} .1919965{col 67}{space 3} .6771166
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .4015595{col 26}{space 2} .1170498{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} .1711643{col 67}{space 3} .6319547
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .6119294{col 26}{space 2} .1221445{col 37}{space 1}    5.01{col 46}{space 3}0.000{col 54}{space 4} .3715059{col 67}{space 3} .8523529
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5736374{col 26}{space 2} .1213316{col 37}{space 1}    4.73{col 46}{space 3}0.000{col 54}{space 4} .3348141{col 67}{space 3} .8124607
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5057656{col 26}{space 2} .1137583{col 37}{space 1}    4.45{col 46}{space 3}0.000{col 54}{space 4} .2818491{col 67}{space 3}  .729682
{txt}{space 9}54  {c |}{col 14}{res}{space 2}  .475725{col 26}{space 2} .1039613{col 37}{space 1}    4.58{col 46}{space 3}0.000{col 54}{space 4} .2710926{col 67}{space 3} .6803573
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{txt}{space 9}57  {c |}{col 14}{res}{space 2} .2732303{col 26}{space 2} .1395532{col 37}{space 1}    1.96{col 46}{space 3}0.051{col 54}{space 4}-.0014595{col 67}{space 3} .5479201
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .2570141{col 26}{space 2} .1288233{col 37}{space 1}    2.00{col 46}{space 3}0.047{col 54}{space 4} .0034445{col 67}{space 3} .5105838
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{txt}{space 9}60  {c |}{col 14}{res}{space 2} .2084832{col 26}{space 2} .0988037{col 37}{space 1}    2.11{col 46}{space 3}0.036{col 54}{space 4} .0140026{col 67}{space 3} .4029637
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{txt}{space 9}62  {c |}{col 14}{res}{space 2} .4220301{col 26}{space 2} .1267598{col 37}{space 1}    3.33{col 46}{space 3}0.001{col 54}{space 4} .1725222{col 67}{space 3} .6715381
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{txt}{space 9}65  {c |}{col 14}{res}{space 2} .2189617{col 26}{space 2} .1073229{col 37}{space 1}    2.04{col 46}{space 3}0.042{col 54}{space 4} .0077124{col 67}{space 3}  .430211
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .6613309{col 26}{space 2} .1262682{col 37}{space 1}    5.24{col 46}{space 3}0.000{col 54}{space 4} .4127906{col 67}{space 3} .9098713
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .5223538{col 26}{space 2} .1180481{col 37}{space 1}    4.42{col 46}{space 3}0.000{col 54}{space 4} .2899937{col 67}{space 3}  .754714
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .6007726{col 26}{space 2}   .11719{col 37}{space 1}    5.13{col 46}{space 3}0.000{col 54}{space 4} .3701013{col 67}{space 3} .8314438
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .5401708{col 26}{space 2} .1307198{col 37}{space 1}    4.13{col 46}{space 3}0.000{col 54}{space 4} .2828681{col 67}{space 3} .7974735
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4638936{col 26}{space 2} .1227173{col 37}{space 1}    3.78{col 46}{space 3}0.000{col 54}{space 4} .2223427{col 67}{space 3} .7054446
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4031114{col 26}{space 2} .1315461{col 37}{space 1}   -3.06{col 46}{space 3}0.002{col 54}{space 4}-.6620405{col 67}{space 3}-.1441823
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    13,186
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       791
{txt}{col 49}F({res}  73{txt},{res}    277{txt}){col 67}= {res}     15.78
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1544
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0952
{txt}{col 49}Root MSE{col 67}= {res}    0.4684

{txt}{ralign 78:(Std. Err. adjusted for {res:278} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0003303{col 26}{space 2} .0122414{col 37}{space 1}   -0.03{col 46}{space 3}0.978{col 54}{space 4}-.0244284{col 67}{space 3} .0237677
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0262362{col 26}{space 2} .0097566{col 37}{space 1}    2.69{col 46}{space 3}0.008{col 54}{space 4} .0070297{col 67}{space 3} .0454426
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0284421{col 26}{space 2} .0027186{col 37}{space 1}   10.46{col 46}{space 3}0.000{col 54}{space 4} .0230905{col 67}{space 3} .0337938
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0003425{col 26}{space 2} .0000375{col 37}{space 1}   -9.14{col 46}{space 3}0.000{col 54}{space 4}-.0004163{col 67}{space 3}-.0002688
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0274609{col 26}{space 2}  .013334{col 37}{space 1}    2.06{col 46}{space 3}0.040{col 54}{space 4}  .001212{col 67}{space 3} .0537097
{txt}{space 6}parent {c |}{col 14}{res}{space 2}  .026328{col 26}{space 2} .0170017{col 37}{space 1}    1.55{col 46}{space 3}0.123{col 54}{space 4}-.0071409{col 67}{space 3} .0597969
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0075244{col 26}{space 2} .0056363{col 37}{space 1}    1.33{col 46}{space 3}0.183{col 54}{space 4}-.0035711{col 67}{space 3} .0186199
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2}  .050012{col 26}{space 2} .0109362{col 37}{space 1}    4.57{col 46}{space 3}0.000{col 54}{space 4} .0284833{col 67}{space 3} .0715407
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.5160856{col 26}{space 2} .3954533{col 37}{space 1}   -1.31{col 46}{space 3}0.193{col 54}{space 4}-1.294561{col 67}{space 3} .2623898
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.5452669{col 26}{space 2} .3151019{col 37}{space 1}   -1.73{col 46}{space 3}0.085{col 54}{space 4}-1.165565{col 67}{space 3} .0750317
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2}-.3990877{col 26}{space 2} .2861904{col 37}{space 1}   -1.39{col 46}{space 3}0.164{col 54}{space 4}-.9624722{col 67}{space 3} .1642969
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.2376116{col 26}{space 2} .3014701{col 37}{space 1}   -0.79{col 46}{space 3}0.431{col 54}{space 4}-.8310751{col 67}{space 3} .3558519
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{txt}{space 9}11  {c |}{col 14}{res}{space 2} .2199576{col 26}{space 2} .2863702{col 37}{space 1}    0.77{col 46}{space 3}0.443{col 54}{space 4}-.3437809{col 67}{space 3}  .783696
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.0002633{col 26}{space 2} .2815324{col 37}{space 1}   -0.00{col 46}{space 3}0.999{col 54}{space 4}-.5544781{col 67}{space 3} .5539516
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    12,861
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       729
{txt}{col 49}{help j_robustsingular##|_new:F(  72,    275)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1432
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0861
{txt}{col 49}Root MSE{col 67}= {res}    0.4738

{txt}{ralign 78:(Std. Err. adjusted for {res:276} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} -.006675{col 26}{space 2} .0129976{col 37}{space 1}   -0.51{col 46}{space 3}0.608{col 54}{space 4}-.0322624{col 67}{space 3} .0189124
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0248005{col 26}{space 2} .0115164{col 37}{space 1}    2.15{col 46}{space 3}0.032{col 54}{space 4}  .002129{col 67}{space 3} .0474721
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0257915{col 26}{space 2} .0027758{col 37}{space 1}    9.29{col 46}{space 3}0.000{col 54}{space 4}  .020327{col 67}{space 3}  .031256
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0003129{col 26}{space 2} .0000348{col 37}{space 1}   -8.98{col 46}{space 3}0.000{col 54}{space 4}-.0003815{col 67}{space 3}-.0002443
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0237224{col 26}{space 2} .0153568{col 37}{space 1}    1.54{col 46}{space 3}0.124{col 54}{space 4}-.0065095{col 67}{space 3} .0539543
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0240941{col 26}{space 2} .0174114{col 37}{space 1}    1.38{col 46}{space 3}0.168{col 54}{space 4}-.0101825{col 67}{space 3} .0583708
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0100693{col 26}{space 2}  .007464{col 37}{space 1}    1.35{col 46}{space 3}0.178{col 54}{space 4}-.0046245{col 67}{space 3} .0247632
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0646308{col 26}{space 2} .0120304{col 37}{space 1}    5.37{col 46}{space 3}0.000{col 54}{space 4} .0409474{col 67}{space 3} .0883141
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.3170028{col 26}{space 2} .0886903{col 37}{space 1}   -3.57{col 46}{space 3}0.000{col 54}{space 4} -.491601{col 67}{space 3}-.1424045
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0045821{col 26}{space 2} .0811767{col 37}{space 1}    0.06{col 46}{space 3}0.955{col 54}{space 4}-.1552247{col 67}{space 3} .1643889
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{txt}{space 10}6  {c |}{col 14}{res}{space 2}  .345301{col 26}{space 2} .1202057{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .1086606{col 67}{space 3} .5819414
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .7325589{col 26}{space 2} .2388351{col 37}{space 1}    3.07{col 46}{space 3}0.002{col 54}{space 4} .2623815{col 67}{space 3} 1.202736
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3644302{col 26}{space 2} .1449875{col 37}{space 1}    2.51{col 46}{space 3}0.013{col 54}{space 4} .0790037{col 67}{space 3} .6498567
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2572371{col 26}{space 2} .1659024{col 37}{space 1}    1.55{col 46}{space 3}0.122{col 54}{space 4} -.069363{col 67}{space 3} .5838373
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .8318135{col 26}{space 2} .1419244{col 37}{space 1}    5.86{col 46}{space 3}0.000{col 54}{space 4} .5524173{col 67}{space 3}  1.11121
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5648535{col 26}{space 2} .1076406{col 37}{space 1}    5.25{col 46}{space 3}0.000{col 54}{space 4} .3529492{col 67}{space 3} .7767577
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .6284643{col 26}{space 2} .1225745{col 37}{space 1}    5.13{col 46}{space 3}0.000{col 54}{space 4} .3871606{col 67}{space 3} .8697679
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5867462{col 26}{space 2} .1057951{col 37}{space 1}    5.55{col 46}{space 3}0.000{col 54}{space 4}  .378475{col 67}{space 3} .7950173
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5298356{col 26}{space 2} .1441642{col 37}{space 1}    3.68{col 46}{space 3}0.000{col 54}{space 4} .2460299{col 67}{space 3} .8136413
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .6423312{col 26}{space 2} .0961527{col 37}{space 1}    6.68{col 46}{space 3}0.000{col 54}{space 4} .4530423{col 67}{space 3} .8316201
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .6699025{col 26}{space 2} .0886348{col 37}{space 1}    7.56{col 46}{space 3}0.000{col 54}{space 4} .4954135{col 67}{space 3} .8443916
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .6322248{col 26}{space 2} .0916931{col 37}{space 1}    6.90{col 46}{space 3}0.000{col 54}{space 4} .4517152{col 67}{space 3} .8127343
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .6167767{col 26}{space 2} .1000675{col 37}{space 1}    6.16{col 46}{space 3}0.000{col 54}{space 4} .4197809{col 67}{space 3} .8137724
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .7524808{col 26}{space 2} .1620458{col 37}{space 1}    4.64{col 46}{space 3}0.000{col 54}{space 4}  .433473{col 67}{space 3} 1.071489
{txt}{space 9}21  {c |}{col 14}{res}{space 2}  .731201{col 26}{space 2}  .291117{col 37}{space 1}    2.51{col 46}{space 3}0.013{col 54}{space 4} .1580999{col 67}{space 3} 1.304302
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .7438491{col 26}{space 2} .2261789{col 37}{space 1}    3.29{col 46}{space 3}0.001{col 54}{space 4} .2985871{col 67}{space 3} 1.189111
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .7286811{col 26}{space 2} .1342636{col 37}{space 1}    5.43{col 46}{space 3}0.000{col 54}{space 4} .4643661{col 67}{space 3} .9929962
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .3955505{col 26}{space 2} .1418361{col 37}{space 1}    2.79{col 46}{space 3}0.006{col 54}{space 4}  .116328{col 67}{space 3}  .674773
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .4148111{col 26}{space 2} .1554637{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4} .1087609{col 67}{space 3} .7208612
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .5115068{col 26}{space 2} .1027142{col 37}{space 1}    4.98{col 46}{space 3}0.000{col 54}{space 4} .3093008{col 67}{space 3} .7137129
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{txt}{space 9}67  {c |}{col 14}{res}{space 2}  .495577{col 26}{space 2} .1123802{col 37}{space 1}    4.41{col 46}{space 3}0.000{col 54}{space 4} .2743422{col 67}{space 3} .7168118
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .5904525{col 26}{space 2} .1176018{col 37}{space 1}    5.02{col 46}{space 3}0.000{col 54}{space 4} .3589383{col 67}{space 3} .8219666
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .7349478{col 26}{space 2} .1083128{col 37}{space 1}    6.79{col 46}{space 3}0.000{col 54}{space 4} .5217203{col 67}{space 3} .9481753
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .5266992{col 26}{space 2}  .141453{col 37}{space 1}    3.72{col 46}{space 3}0.000{col 54}{space 4} .2482308{col 67}{space 3} .8051675
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4862489{col 26}{space 2} .0960433{col 37}{space 1}   -5.06{col 46}{space 3}0.000{col 54}{space 4}-.6753224{col 67}{space 3}-.2971755
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    12,387
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       401
{txt}{col 49}F({res}  72{txt},{res}    173{txt}){col 67}= {res}    111.22
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1199
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0850
{txt}{col 49}Root MSE{col 67}= {res}    0.4766

{txt}{ralign 78:(Std. Err. adjusted for {res:174} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0141072{col 26}{space 2} .0171872{col 37}{space 1}   -0.82{col 46}{space 3}0.413{col 54}{space 4}-.0480309{col 67}{space 3} .0198165
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0084493{col 26}{space 2} .0078007{col 37}{space 1}    1.08{col 46}{space 3}0.280{col 54}{space 4}-.0069476{col 67}{space 3} .0238461
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0238645{col 26}{space 2} .0021914{col 37}{space 1}   10.89{col 46}{space 3}0.000{col 54}{space 4} .0195392{col 67}{space 3} .0281899
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002697{col 26}{space 2} .0000278{col 37}{space 1}   -9.71{col 46}{space 3}0.000{col 54}{space 4}-.0003245{col 67}{space 3}-.0002149
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0055444{col 26}{space 2}  .013794{col 37}{space 1}   -0.40{col 46}{space 3}0.688{col 54}{space 4}-.0327706{col 67}{space 3} .0216818
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0138707{col 26}{space 2} .0179139{col 37}{space 1}    0.77{col 46}{space 3}0.440{col 54}{space 4}-.0214872{col 67}{space 3} .0492286
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0146688{col 26}{space 2}   .00631{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4} .0022143{col 67}{space 3} .0271232
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0696255{col 26}{space 2} .0120691{col 37}{space 1}    5.77{col 46}{space 3}0.000{col 54}{space 4} .0458039{col 67}{space 3} .0934471
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0571635{col 26}{space 2} .1005145{col 37}{space 1}   -0.57{col 46}{space 3}0.570{col 54}{space 4}-.2555561{col 67}{space 3} .1412291
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{txt}{space 9}11  {c |}{col 14}{res}{space 2} .3648879{col 26}{space 2} .0977473{col 37}{space 1}    3.73{col 46}{space 3}0.000{col 54}{space 4} .1719571{col 67}{space 3} .5578186
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .3831705{col 26}{space 2} .1087266{col 37}{space 1}    3.52{col 46}{space 3}0.001{col 54}{space 4}  .168569{col 67}{space 3}  .597772
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .2142994{col 26}{space 2} .1591477{col 37}{space 1}    1.35{col 46}{space 3}0.180{col 54}{space 4}-.0998217{col 67}{space 3} .5284206
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .2717742{col 26}{space 2} .1219037{col 37}{space 1}    2.23{col 46}{space 3}0.027{col 54}{space 4} .0311641{col 67}{space 3} .5123842
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .2679418{col 26}{space 2} .1453244{col 37}{space 1}    1.84{col 46}{space 3}0.067{col 54}{space 4}-.0188952{col 67}{space 3} .5547789
{txt}{space 9}16  {c |}{col 14}{res}{space 2}  .361605{col 26}{space 2}  .039639{col 37}{space 1}    9.12{col 46}{space 3}0.000{col 54}{space 4} .2833665{col 67}{space 3} .4398434
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3655517{col 26}{space 2} .0567047{col 37}{space 1}    6.45{col 46}{space 3}0.000{col 54}{space 4} .2536296{col 67}{space 3} .4774737
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .3716911{col 26}{space 2} .0458617{col 37}{space 1}    8.10{col 46}{space 3}0.000{col 54}{space 4} .2811705{col 67}{space 3} .4622117
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .2078344{col 26}{space 2} .0685864{col 37}{space 1}    3.03{col 46}{space 3}0.003{col 54}{space 4} .0724605{col 67}{space 3} .3432083
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .4124888{col 26}{space 2} .1050335{col 37}{space 1}    3.93{col 46}{space 3}0.000{col 54}{space 4} .2051766{col 67}{space 3}  .619801
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{txt}{space 9}31  {c |}{col 14}{res}{space 2} .3107649{col 26}{space 2} .0605493{col 37}{space 1}    5.13{col 46}{space 3}0.000{col 54}{space 4} .1912545{col 67}{space 3} .4302754
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .3134043{col 26}{space 2} .0559886{col 37}{space 1}    5.60{col 46}{space 3}0.000{col 54}{space 4} .2028957{col 67}{space 3}  .423913
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{txt}{space 9}38  {c |}{col 14}{res}{space 2} .3524615{col 26}{space 2} .0520347{col 37}{space 1}    6.77{col 46}{space 3}0.000{col 54}{space 4} .2497569{col 67}{space 3}  .455166
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .2843574{col 26}{space 2}  .066339{col 37}{space 1}    4.29{col 46}{space 3}0.000{col 54}{space 4} .1534194{col 67}{space 3} .4152954
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{txt}{space 9}41  {c |}{col 14}{res}{space 2} .2789001{col 26}{space 2} .0444589{col 37}{space 1}    6.27{col 46}{space 3}0.000{col 54}{space 4} .1911483{col 67}{space 3} .3666518
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .2533984{col 26}{space 2} .0437616{col 37}{space 1}    5.79{col 46}{space 3}0.000{col 54}{space 4} .1670229{col 67}{space 3} .3397738
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .2332362{col 26}{space 2} .0451033{col 37}{space 1}    5.17{col 46}{space 3}0.000{col 54}{space 4} .1442126{col 67}{space 3} .3222598
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .1171655{col 26}{space 2}  .064137{col 37}{space 1}    1.83{col 46}{space 3}0.069{col 54}{space 4}-.0094264{col 67}{space 3} .2437573
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .1369172{col 26}{space 2} .0667753{col 37}{space 1}    2.05{col 46}{space 3}0.042{col 54}{space 4} .0051182{col 67}{space 3} .2687163
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .3706841{col 26}{space 2} .0751672{col 37}{space 1}    4.93{col 46}{space 3}0.000{col 54}{space 4} .2223213{col 67}{space 3}  .519047
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1828552{col 26}{space 2}  .051618{col 37}{space 1}   -3.54{col 46}{space 3}0.001{col 54}{space 4}-.2847374{col 67}{space 3} -.080973
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare $cov i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    11,556
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       681
{txt}{col 49}F({res}  77{txt},{res}    242{txt}){col 67}= {res}    106.34
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1633
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1046
{txt}{col 49}Root MSE{col 67}= {res}    0.4697

{txt}{ralign 78:(Std. Err. adjusted for {res:243} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0164734{col 26}{space 2} .0074921{col 37}{space 1}    2.20{col 46}{space 3}0.029{col 54}{space 4} .0017153{col 67}{space 3} .0312315
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{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002166{col 26}{space 2}  .000032{col 37}{space 1}   -6.76{col 46}{space 3}0.000{col 54}{space 4}-.0002797{col 67}{space 3}-.0001535
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{txt}{space 4}children {c |}{col 14}{res}{space 2}  .002706{col 26}{space 2}  .007794{col 37}{space 1}    0.35{col 46}{space 3}0.729{col 54}{space 4}-.0126468{col 67}{space 3} .0180588
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0648108{col 26}{space 2} .0115966{col 37}{space 1}    5.59{col 46}{space 3}0.000{col 54}{space 4} .0419677{col 67}{space 3} .0876539
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}64  {c |}{col 14}{res}{space 2} .4888047{col 26}{space 2} .1107876{col 37}{space 1}    4.41{col 46}{space 3}0.000{col 54}{space 4} .2705736{col 67}{space 3} .7070357
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .4460215{col 26}{space 2} .0966948{col 37}{space 1}    4.61{col 46}{space 3}0.000{col 54}{space 4} .2555505{col 67}{space 3} .6364925
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .7776707{col 26}{space 2} .0873549{col 37}{space 1}    8.90{col 46}{space 3}0.000{col 54}{space 4} .6055977{col 67}{space 3} .9497437
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .6250893{col 26}{space 2} .0927251{col 37}{space 1}    6.74{col 46}{space 3}0.000{col 54}{space 4} .4424379{col 67}{space 3} .8077407
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .7260127{col 26}{space 2} .0948559{col 37}{space 1}    7.65{col 46}{space 3}0.000{col 54}{space 4} .5391642{col 67}{space 3} .9128613
{txt}{space 9}69  {c |}{col 14}{res}{space 2}  .578858{col 26}{space 2} .0857779{col 37}{space 1}    6.75{col 46}{space 3}0.000{col 54}{space 4} .4098915{col 67}{space 3} .7478245
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .5401246{col 26}{space 2} .0957411{col 37}{space 1}    5.64{col 46}{space 3}0.000{col 54}{space 4} .3515323{col 67}{space 3}  .728717
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3962043{col 26}{space 2} .0811697{col 37}{space 1}   -4.88{col 46}{space 3}0.000{col 54}{space 4}-.5560935{col 67}{space 3} -.236315
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\regwithlongsample.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\regwithlongsample.tex"'})

{com}. 
. restore
{txt}
{com}. 
. 
. 
. ///////////////////////////////////
> ///Short run effect split sample men and women - split sample
> 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if sex==1, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    35,444
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       929
{txt}{col 49}F({res}  76{txt},{res}    283{txt}){col 67}= {res}    103.27
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1094
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0835
{txt}{col 49}Root MSE{col 67}= {res}    0.4783

{txt}{ralign 78:(Std. Err. adjusted for {res:284} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0090715{col 26}{space 2} .0068981{col 37}{space 1}    1.32{col 46}{space 3}0.190{col 54}{space 4}-.0045067{col 67}{space 3} .0226497
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0204126{col 26}{space 2} .0015961{col 37}{space 1}   12.79{col 46}{space 3}0.000{col 54}{space 4} .0172709{col 67}{space 3} .0235543
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002206{col 26}{space 2} .0000181{col 37}{space 1}  -12.17{col 46}{space 3}0.000{col 54}{space 4}-.0002563{col 67}{space 3}-.0001849
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0034269{col 26}{space 2} .0070248{col 37}{space 1}    0.49{col 46}{space 3}0.626{col 54}{space 4}-.0104005{col 67}{space 3} .0172544
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0507437{col 26}{space 2} .0105475{col 37}{space 1}    4.81{col 46}{space 3}0.000{col 54}{space 4} .0299823{col 67}{space 3} .0715051
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0144057{col 26}{space 2} .0040512{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .0064314{col 67}{space 3}   .02238
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0788715{col 26}{space 2} .0059334{col 37}{space 1}   13.29{col 46}{space 3}0.000{col 54}{space 4} .0671923{col 67}{space 3} .0905507
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .6303686{col 26}{space 2}  .382934{col 37}{space 1}    1.65{col 46}{space 3}0.101{col 54}{space 4}-.1233918{col 67}{space 3} 1.384129
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4466708{col 26}{space 2} .2377636{col 37}{space 1}    1.88{col 46}{space 3}0.061{col 54}{space 4}-.0213387{col 67}{space 3} .9146803
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3587313{col 26}{space 2} .2824292{col 37}{space 1}    1.27{col 46}{space 3}0.205{col 54}{space 4}-.1971973{col 67}{space 3} .9146598
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2293669{col 26}{space 2} .1808588{col 37}{space 1}    1.27{col 46}{space 3}0.206{col 54}{space 4}-.1266322{col 67}{space 3}  .585366
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .3633305{col 26}{space 2} .0542437{col 37}{space 1}    6.70{col 46}{space 3}0.000{col 54}{space 4} .2565583{col 67}{space 3} .4701028
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .4104612{col 26}{space 2} .0487559{col 37}{space 1}    8.42{col 46}{space 3}0.000{col 54}{space 4}  .314491{col 67}{space 3} .5064315
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .3515264{col 26}{space 2} .0498845{col 37}{space 1}    7.05{col 46}{space 3}0.000{col 54}{space 4} .2533346{col 67}{space 3} .4497182
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3442864{col 26}{space 2} .0360471{col 37}{space 1}    9.55{col 46}{space 3}0.000{col 54}{space 4}  .273332{col 67}{space 3} .4152408
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .3553778{col 26}{space 2} .0495818{col 37}{space 1}    7.17{col 46}{space 3}0.000{col 54}{space 4} .2577818{col 67}{space 3} .4529737
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .7754583{col 26}{space 2} .0724034{col 37}{space 1}   10.71{col 46}{space 3}0.000{col 54}{space 4} .6329408{col 67}{space 3} .9179757
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .6034337{col 26}{space 2} .0566729{col 37}{space 1}   10.65{col 46}{space 3}0.000{col 54}{space 4} .4918799{col 67}{space 3} .7149876
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5955035{col 26}{space 2}  .068196{col 37}{space 1}    8.73{col 46}{space 3}0.000{col 54}{space 4} .4612678{col 67}{space 3} .7297392
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .6240469{col 26}{space 2} .0583397{col 37}{space 1}   10.70{col 46}{space 3}0.000{col 54}{space 4} .5092121{col 67}{space 3} .7388818
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5475357{col 26}{space 2} .0718472{col 37}{space 1}    7.62{col 46}{space 3}0.000{col 54}{space 4}  .406113{col 67}{space 3} .6889584
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .6933271{col 26}{space 2} .0316899{col 37}{space 1}   21.88{col 46}{space 3}0.000{col 54}{space 4} .6309492{col 67}{space 3}  .755705
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .6608342{col 26}{space 2} .0306972{col 37}{space 1}   21.53{col 46}{space 3}0.000{col 54}{space 4} .6004103{col 67}{space 3} .7212581
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .6525089{col 26}{space 2} .0283964{col 37}{space 1}   22.98{col 46}{space 3}0.000{col 54}{space 4} .5966139{col 67}{space 3} .7084039
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .5674722{col 26}{space 2} .0351716{col 37}{space 1}   16.13{col 46}{space 3}0.000{col 54}{space 4} .4982411{col 67}{space 3} .6367033
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .6905755{col 26}{space 2} .0609534{col 37}{space 1}   11.33{col 46}{space 3}0.000{col 54}{space 4} .5705961{col 67}{space 3}  .810555
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .6172597{col 26}{space 2} .0445031{col 37}{space 1}   13.87{col 46}{space 3}0.000{col 54}{space 4} .5296606{col 67}{space 3} .7048588
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .5339886{col 26}{space 2} .0487195{col 37}{space 1}   10.96{col 46}{space 3}0.000{col 54}{space 4}   .43809{col 67}{space 3} .6298872
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .6437398{col 26}{space 2} .0338736{col 37}{space 1}   19.00{col 46}{space 3}0.000{col 54}{space 4} .5770637{col 67}{space 3} .7104159
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .5737735{col 26}{space 2} .0324284{col 37}{space 1}   17.69{col 46}{space 3}0.000{col 54}{space 4} .5099419{col 67}{space 3}  .637605
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5878052{col 26}{space 2} .0443493{col 37}{space 1}   13.25{col 46}{space 3}0.000{col 54}{space 4} .5005088{col 67}{space 3} .6751015
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .6211773{col 26}{space 2} .0351289{col 37}{space 1}   17.68{col 46}{space 3}0.000{col 54}{space 4} .5520302{col 67}{space 3} .6903245
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .6648599{col 26}{space 2} .0371971{col 37}{space 1}   17.87{col 46}{space 3}0.000{col 54}{space 4} .5916419{col 67}{space 3}  .738078
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .5315975{col 26}{space 2} .0307743{col 37}{space 1}   17.27{col 46}{space 3}0.000{col 54}{space 4}  .471022{col 67}{space 3} .5921731
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .4910322{col 26}{space 2} .0321357{col 37}{space 1}   15.28{col 46}{space 3}0.000{col 54}{space 4} .4277768{col 67}{space 3} .5542876
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .4106524{col 26}{space 2} .0345783{col 37}{space 1}   11.88{col 46}{space 3}0.000{col 54}{space 4} .3425891{col 67}{space 3} .4787156
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .5900644{col 26}{space 2} .0381223{col 37}{space 1}   15.48{col 46}{space 3}0.000{col 54}{space 4} .5150252{col 67}{space 3} .6651036
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .5806696{col 26}{space 2} .0304888{col 37}{space 1}   19.05{col 46}{space 3}0.000{col 54}{space 4} .5206559{col 67}{space 3} .6406832
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .5627793{col 26}{space 2} .0304865{col 37}{space 1}   18.46{col 46}{space 3}0.000{col 54}{space 4} .5027702{col 67}{space 3} .6227885
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .4468025{col 26}{space 2} .0285845{col 37}{space 1}   15.63{col 46}{space 3}0.000{col 54}{space 4} .3905373{col 67}{space 3} .5030677
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .4777534{col 26}{space 2} .0295108{col 37}{space 1}   16.19{col 46}{space 3}0.000{col 54}{space 4} .4196649{col 67}{space 3} .5358419
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .6112257{col 26}{space 2} .0621422{col 37}{space 1}    9.84{col 46}{space 3}0.000{col 54}{space 4} .4889061{col 67}{space 3} .7335453
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .5275611{col 26}{space 2} .0490768{col 37}{space 1}   10.75{col 46}{space 3}0.000{col 54}{space 4} .4309592{col 67}{space 3} .6241631
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .6427646{col 26}{space 2} .0329732{col 37}{space 1}   19.49{col 46}{space 3}0.000{col 54}{space 4} .5778608{col 67}{space 3} .7076685
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{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5804588{col 26}{space 2}  .043295{col 37}{space 1}   13.41{col 46}{space 3}0.000{col 54}{space 4} .4952378{col 67}{space 3} .6656799
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .6222985{col 26}{space 2} .0249352{col 37}{space 1}   24.96{col 46}{space 3}0.000{col 54}{space 4} .5732165{col 67}{space 3} .6713805
{txt}{space 9}42  {c |}{col 14}{res}{space 2}  .627457{col 26}{space 2} .0261991{col 37}{space 1}   23.95{col 46}{space 3}0.000{col 54}{space 4} .5758872{col 67}{space 3} .6790269
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .5616295{col 26}{space 2} .0269314{col 37}{space 1}   20.85{col 46}{space 3}0.000{col 54}{space 4} .5086182{col 67}{space 3} .6146408
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .5350114{col 26}{space 2} .0301404{col 37}{space 1}   17.75{col 46}{space 3}0.000{col 54}{space 4} .4756835{col 67}{space 3} .5943392
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .5322732{col 26}{space 2} .0271865{col 37}{space 1}   19.58{col 46}{space 3}0.000{col 54}{space 4} .4787597{col 67}{space 3} .5857867
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .6037435{col 26}{space 2} .0327762{col 37}{space 1}   18.42{col 46}{space 3}0.000{col 54}{space 4} .5392274{col 67}{space 3} .6682595
{txt}{space 9}47  {c |}{col 14}{res}{space 2}  .584415{col 26}{space 2} .0255165{col 37}{space 1}   22.90{col 46}{space 3}0.000{col 54}{space 4} .5341888{col 67}{space 3} .6346411
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .6061981{col 26}{space 2} .0294238{col 37}{space 1}   20.60{col 46}{space 3}0.000{col 54}{space 4} .5482809{col 67}{space 3} .6641154
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .5354797{col 26}{space 2} .0302389{col 37}{space 1}   17.71{col 46}{space 3}0.000{col 54}{space 4} .4759581{col 67}{space 3} .5950013
{txt}{space 9}50  {c |}{col 14}{res}{space 2}   .54223{col 26}{space 2} .0253092{col 37}{space 1}   21.42{col 46}{space 3}0.000{col 54}{space 4} .4924119{col 67}{space 3} .5920481
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .6678051{col 26}{space 2} .0395652{col 37}{space 1}   16.88{col 46}{space 3}0.000{col 54}{space 4} .5899256{col 67}{space 3} .7456846
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .6088557{col 26}{space 2} .0416742{col 37}{space 1}   14.61{col 46}{space 3}0.000{col 54}{space 4} .5268249{col 67}{space 3} .6908865
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5763869{col 26}{space 2} .0354366{col 37}{space 1}   16.27{col 46}{space 3}0.000{col 54}{space 4} .5066343{col 67}{space 3} .6461396
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .5410105{col 26}{space 2} .0460307{col 37}{space 1}   11.75{col 46}{space 3}0.000{col 54}{space 4} .4504046{col 67}{space 3} .6316165
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .6025117{col 26}{space 2} .0477134{col 37}{space 1}   12.63{col 46}{space 3}0.000{col 54}{space 4} .5085935{col 67}{space 3}   .69643
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .3338956{col 26}{space 2} .0388029{col 37}{space 1}    8.60{col 46}{space 3}0.000{col 54}{space 4} .2575166{col 67}{space 3} .4102746
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .2838489{col 26}{space 2} .0281296{col 37}{space 1}   10.09{col 46}{space 3}0.000{col 54}{space 4}  .228479{col 67}{space 3} .3392188
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .3022703{col 26}{space 2} .0397223{col 37}{space 1}    7.61{col 46}{space 3}0.000{col 54}{space 4} .2240816{col 67}{space 3}  .380459
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .2482658{col 26}{space 2} .0312425{col 37}{space 1}    7.95{col 46}{space 3}0.000{col 54}{space 4} .1867687{col 67}{space 3} .3097629
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .2813253{col 26}{space 2} .0323884{col 37}{space 1}    8.69{col 46}{space 3}0.000{col 54}{space 4} .2175725{col 67}{space 3} .3450781
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5370839{col 26}{space 2}  .070475{col 37}{space 1}    7.62{col 46}{space 3}0.000{col 54}{space 4} .3983622{col 67}{space 3} .6758056
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .4659473{col 26}{space 2}  .050022{col 37}{space 1}    9.31{col 46}{space 3}0.000{col 54}{space 4} .3674849{col 67}{space 3} .5644098
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .4549214{col 26}{space 2} .0428577{col 37}{space 1}   10.61{col 46}{space 3}0.000{col 54}{space 4} .3705611{col 67}{space 3} .5392817
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .3898405{col 26}{space 2} .0420112{col 37}{space 1}    9.28{col 46}{space 3}0.000{col 54}{space 4} .3071464{col 67}{space 3} .4725346
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .3054128{col 26}{space 2} .0385319{col 37}{space 1}    7.93{col 46}{space 3}0.000{col 54}{space 4} .2295674{col 67}{space 3} .3812582
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .7326583{col 26}{space 2}  .061936{col 37}{space 1}   11.83{col 46}{space 3}0.000{col 54}{space 4} .6107446{col 67}{space 3}  .854572
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .6077082{col 26}{space 2} .0502482{col 37}{space 1}   12.09{col 46}{space 3}0.000{col 54}{space 4} .5088006{col 67}{space 3} .7066159
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .6972946{col 26}{space 2} .0497285{col 37}{space 1}   14.02{col 46}{space 3}0.000{col 54}{space 4}   .59941{col 67}{space 3} .7951792
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6158604{col 26}{space 2} .0414999{col 37}{space 1}   14.84{col 46}{space 3}0.000{col 54}{space 4} .5341728{col 67}{space 3}  .697548
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .5697396{col 26}{space 2} .0496313{col 37}{space 1}   11.48{col 46}{space 3}0.000{col 54}{space 4} .4720462{col 67}{space 3}  .667433
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4753851{col 26}{space 2} .0408306{col 37}{space 1}  -11.64{col 46}{space 3}0.000{col 54}{space 4}-.5557553{col 67}{space 3}-.3950148
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum<0.25 & sex==1, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     9,633
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       714
{txt}{col 49}{help j_robustsingular##|_new:F(  71,    269)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1566
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0818
{txt}{col 49}Root MSE{col 67}= {res}    0.4762

{txt}{ralign 78:(Std. Err. adjusted for {res:270} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0046363{col 26}{space 2} .0095686{col 37}{space 1}   -0.48{col 46}{space 3}0.628{col 54}{space 4}-.0234752{col 67}{space 3} .0142026
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0240627{col 26}{space 2} .0027338{col 37}{space 1}    8.80{col 46}{space 3}0.000{col 54}{space 4} .0186804{col 67}{space 3}  .029445
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002676{col 26}{space 2} .0000347{col 37}{space 1}   -7.71{col 46}{space 3}0.000{col 54}{space 4}-.0003359{col 67}{space 3}-.0001993
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0304127{col 26}{space 2} .0166693{col 37}{space 1}    1.82{col 46}{space 3}0.069{col 54}{space 4}-.0024062{col 67}{space 3} .0632315
{txt}{space 6}parent {c |}{col 14}{res}{space 2}  .023284{col 26}{space 2} .0211355{col 37}{space 1}    1.10{col 46}{space 3}0.272{col 54}{space 4} -.018328{col 67}{space 3}  .064896
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0146222{col 26}{space 2} .0065166{col 37}{space 1}    2.24{col 46}{space 3}0.026{col 54}{space 4} .0017922{col 67}{space 3} .0274521
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0560917{col 26}{space 2} .0129851{col 37}{space 1}    4.32{col 46}{space 3}0.000{col 54}{space 4} .0305264{col 67}{space 3}  .081657
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}6  {c |}{col 14}{res}{space 2}   -.1363{col 26}{space 2} .4326595{col 37}{space 1}   -0.32{col 46}{space 3}0.753{col 54}{space 4}-.9881296{col 67}{space 3} .7155296
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .9281373{col 26}{space 2} .4210073{col 37}{space 1}    2.20{col 46}{space 3}0.028{col 54}{space 4} .0992489{col 67}{space 3} 1.757026
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0403075{col 26}{space 2}  .429964{col 37}{space 1}    0.09{col 46}{space 3}0.925{col 54}{space 4}-.8062151{col 67}{space 3} .8868301
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1466974{col 26}{space 2} .4511593{col 37}{space 1}    0.33{col 46}{space 3}0.745{col 54}{space 4}-.7415551{col 67}{space 3}  1.03495
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2358218{col 26}{space 2} .4318815{col 37}{space 1}    0.55{col 46}{space 3}0.585{col 54}{space 4} -.614476{col 67}{space 3}  1.08612
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .7530993{col 26}{space 2} .4328094{col 37}{space 1}    1.74{col 46}{space 3}0.083{col 54}{space 4}-.0990254{col 67}{space 3} 1.605224
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5500479{col 26}{space 2} .4264578{col 37}{space 1}    1.29{col 46}{space 3}0.198{col 54}{space 4}-.2895715{col 67}{space 3} 1.389667
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .4034072{col 26}{space 2} .4360042{col 37}{space 1}    0.93{col 46}{space 3}0.356{col 54}{space 4}-.4550074{col 67}{space 3} 1.261822
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .4504217{col 26}{space 2} .4293084{col 37}{space 1}    1.05{col 46}{space 3}0.295{col 54}{space 4}-.3948102{col 67}{space 3} 1.295654
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .4558178{col 26}{space 2} .4338084{col 37}{space 1}    1.05{col 46}{space 3}0.294{col 54}{space 4}-.3982737{col 67}{space 3} 1.309909
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .6018623{col 26}{space 2} .4207952{col 37}{space 1}    1.43{col 46}{space 3}0.154{col 54}{space 4}-.2266085{col 67}{space 3} 1.430333
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .5382608{col 26}{space 2}   .42103{col 37}{space 1}    1.28{col 46}{space 3}0.202{col 54}{space 4}-.2906724{col 67}{space 3} 1.367194
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5052831{col 26}{space 2} .4203074{col 37}{space 1}    1.20{col 46}{space 3}0.230{col 54}{space 4}-.3222272{col 67}{space 3} 1.332794
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .4627838{col 26}{space 2} .4226404{col 37}{space 1}    1.09{col 46}{space 3}0.275{col 54}{space 4}-.3693199{col 67}{space 3} 1.294887
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .3848512{col 26}{space 2} .4473334{col 37}{space 1}    0.86{col 46}{space 3}0.390{col 54}{space 4}-.4958687{col 67}{space 3} 1.265571
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .6553403{col 26}{space 2} .4634139{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.2570391{col 67}{space 3}  1.56772
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .4260384{col 26}{space 2} .5340256{col 37}{space 1}    0.80{col 46}{space 3}0.426{col 54}{space 4}-.6253629{col 67}{space 3}  1.47744
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .7782478{col 26}{space 2}  .428017{col 37}{space 1}    1.82{col 46}{space 3}0.070{col 54}{space 4}-.0644415{col 67}{space 3} 1.620937
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .3336052{col 26}{space 2} .4310849{col 37}{space 1}    0.77{col 46}{space 3}0.440{col 54}{space 4}-.5151241{col 67}{space 3} 1.182335
{txt}{space 9}25  {c |}{col 14}{res}{space 2}  .286009{col 26}{space 2} .4636663{col 37}{space 1}    0.62{col 46}{space 3}0.538{col 54}{space 4}-.6268674{col 67}{space 3} 1.198885
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .4711001{col 26}{space 2}  .422229{col 37}{space 1}    1.12{col 46}{space 3}0.266{col 54}{space 4}-.3601936{col 67}{space 3} 1.302394
{txt}{space 9}27  {c |}{col 14}{res}{space 2}   .53733{col 26}{space 2} .4244318{col 37}{space 1}    1.27{col 46}{space 3}0.207{col 54}{space 4}-.2983006{col 67}{space 3} 1.372961
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .3946577{col 26}{space 2} .4178498{col 37}{space 1}    0.94{col 46}{space 3}0.346{col 54}{space 4}-.4280142{col 67}{space 3} 1.217329
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .3741906{col 26}{space 2} .4215547{col 37}{space 1}    0.89{col 46}{space 3}0.376{col 54}{space 4}-.4557755{col 67}{space 3} 1.204157
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .2834021{col 26}{space 2} .4208776{col 37}{space 1}    0.67{col 46}{space 3}0.501{col 54}{space 4}-.5452311{col 67}{space 3} 1.112035
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .5890782{col 26}{space 2} .4282218{col 37}{space 1}    1.38{col 46}{space 3}0.170{col 54}{space 4}-.2540142{col 67}{space 3} 1.432171
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .5150625{col 26}{space 2} .4220488{col 37}{space 1}    1.22{col 46}{space 3}0.223{col 54}{space 4}-.3158764{col 67}{space 3} 1.346001
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .4786798{col 26}{space 2} .4212477{col 37}{space 1}    1.14{col 46}{space 3}0.257{col 54}{space 4}-.3506819{col 67}{space 3} 1.308042
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .3283616{col 26}{space 2}   .42064{col 37}{space 1}    0.78{col 46}{space 3}0.436{col 54}{space 4}-.4998037{col 67}{space 3} 1.156527
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .3072602{col 26}{space 2} .4193693{col 37}{space 1}    0.73{col 46}{space 3}0.464{col 54}{space 4}-.5184033{col 67}{space 3} 1.132924
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .5106923{col 26}{space 2} .4586304{col 37}{space 1}    1.11{col 46}{space 3}0.266{col 54}{space 4}-.3922692{col 67}{space 3} 1.413654
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .4465345{col 26}{space 2} .4382441{col 37}{space 1}    1.02{col 46}{space 3}0.309{col 54}{space 4}-.4162902{col 67}{space 3} 1.309359
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .6643288{col 26}{space 2} .4245965{col 37}{space 1}    1.56{col 46}{space 3}0.119{col 54}{space 4}-.1716262{col 67}{space 3} 1.500284
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .3437358{col 26}{space 2} .4425948{col 37}{space 1}    0.78{col 46}{space 3}0.438{col 54}{space 4}-.5276546{col 67}{space 3} 1.215126
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .4105089{col 26}{space 2} .4436615{col 37}{space 1}    0.93{col 46}{space 3}0.356{col 54}{space 4}-.4629816{col 67}{space 3} 1.283999
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .5317294{col 26}{space 2} .4176274{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.2905045{col 67}{space 3} 1.353963
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .5655193{col 26}{space 2} .4207427{col 37}{space 1}    1.34{col 46}{space 3}0.180{col 54}{space 4}-.2628483{col 67}{space 3} 1.393887
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .4579248{col 26}{space 2} .4198117{col 37}{space 1}    1.09{col 46}{space 3}0.276{col 54}{space 4}-.3686096{col 67}{space 3} 1.284459
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .4404678{col 26}{space 2} .4182437{col 37}{space 1}    1.05{col 46}{space 3}0.293{col 54}{space 4}-.3829796{col 67}{space 3} 1.263915
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .4643657{col 26}{space 2}  .422398{col 37}{space 1}    1.10{col 46}{space 3}0.273{col 54}{space 4}-.3672608{col 67}{space 3} 1.295992
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .5250392{col 26}{space 2} .4220532{col 37}{space 1}    1.24{col 46}{space 3}0.215{col 54}{space 4}-.3059085{col 67}{space 3} 1.355987
{txt}{space 9}47  {c |}{col 14}{res}{space 2}  .481245{col 26}{space 2} .4168397{col 37}{space 1}    1.15{col 46}{space 3}0.249{col 54}{space 4}-.3394382{col 67}{space 3} 1.301928
{txt}{space 9}48  {c |}{col 14}{res}{space 2}  .511162{col 26}{space 2} .4178927{col 37}{space 1}    1.22{col 46}{space 3}0.222{col 54}{space 4}-.3115944{col 67}{space 3} 1.333918
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .4457641{col 26}{space 2} .4161938{col 37}{space 1}    1.07{col 46}{space 3}0.285{col 54}{space 4}-.3736474{col 67}{space 3} 1.265176
{txt}{space 9}50  {c |}{col 14}{res}{space 2}  .418646{col 26}{space 2} .4185495{col 37}{space 1}    1.00{col 46}{space 3}0.318{col 54}{space 4}-.4054034{col 67}{space 3} 1.242696
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .6216955{col 26}{space 2} .4329812{col 37}{space 1}    1.44{col 46}{space 3}0.152{col 54}{space 4}-.2307674{col 67}{space 3} 1.474158
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5007477{col 26}{space 2}  .445465{col 37}{space 1}    1.12{col 46}{space 3}0.262{col 54}{space 4}-.3762936{col 67}{space 3} 1.377789
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5327422{col 26}{space 2} .4332842{col 37}{space 1}    1.23{col 46}{space 3}0.220{col 54}{space 4}-.3203173{col 67}{space 3} 1.385802
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .4654831{col 26}{space 2} .4234949{col 37}{space 1}    1.10{col 46}{space 3}0.273{col 54}{space 4} -.368303{col 67}{space 3} 1.299269
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .5011167{col 26}{space 2} .4303759{col 37}{space 1}    1.16{col 46}{space 3}0.245{col 54}{space 4}-.3462168{col 67}{space 3}  1.34845
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .1693504{col 26}{space 2} .4411065{col 37}{space 1}    0.38{col 46}{space 3}0.701{col 54}{space 4}-.6991097{col 67}{space 3} 1.037811
{txt}{space 9}57  {c |}{col 14}{res}{space 2}  .488079{col 26}{space 2} .4412792{col 37}{space 1}    1.11{col 46}{space 3}0.270{col 54}{space 4}-.3807211{col 67}{space 3} 1.356879
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .1893501{col 26}{space 2} .4221874{col 37}{space 1}    0.45{col 46}{space 3}0.654{col 54}{space 4}-.6418617{col 67}{space 3} 1.020562
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .2720425{col 26}{space 2} .4309006{col 37}{space 1}    0.63{col 46}{space 3}0.528{col 54}{space 4} -.576324{col 67}{space 3} 1.120409
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .2490195{col 26}{space 2} .4245237{col 37}{space 1}    0.59{col 46}{space 3}0.558{col 54}{space 4}-.5867921{col 67}{space 3} 1.084831
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .1501887{col 26}{space 2} .4299857{col 37}{space 1}    0.35{col 46}{space 3}0.727{col 54}{space 4}-.6963767{col 67}{space 3} .9967541
{txt}{space 9}62  {c |}{col 14}{res}{space 2}  .321583{col 26}{space 2} .4746571{col 37}{space 1}    0.68{col 46}{space 3}0.499{col 54}{space 4}-.6129324{col 67}{space 3} 1.256098
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .1542405{col 26}{space 2} .4239206{col 37}{space 1}    0.36{col 46}{space 3}0.716{col 54}{space 4}-.6803836{col 67}{space 3} .9888647
{txt}{space 9}64  {c |}{col 14}{res}{space 2}  .296967{col 26}{space 2} .4250546{col 37}{space 1}    0.70{col 46}{space 3}0.485{col 54}{space 4}-.5398899{col 67}{space 3} 1.133824
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .2729851{col 26}{space 2} .4252358{col 37}{space 1}    0.64{col 46}{space 3}0.521{col 54}{space 4}-.5642285{col 67}{space 3} 1.110199
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .6274879{col 26}{space 2} .4589322{col 37}{space 1}    1.37{col 46}{space 3}0.173{col 54}{space 4} -.276068{col 67}{space 3} 1.531044
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .4794903{col 26}{space 2}  .436044{col 37}{space 1}    1.10{col 46}{space 3}0.272{col 54}{space 4}-.3790026{col 67}{space 3} 1.337983
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .6251707{col 26}{space 2} .4302612{col 37}{space 1}    1.45{col 46}{space 3}0.147{col 54}{space 4}-.2219369{col 67}{space 3} 1.472278
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .5416081{col 26}{space 2} .4424728{col 37}{space 1}    1.22{col 46}{space 3}0.222{col 54}{space 4} -.329542{col 67}{space 3} 1.412758
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4679697{col 26}{space 2}  .431457{col 37}{space 1}    1.08{col 46}{space 3}0.279{col 54}{space 4}-.3814923{col 67}{space 3} 1.317432
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4132954{col 26}{space 2} .4210989{col 37}{space 1}   -0.98{col 46}{space 3}0.327{col 54}{space 4}-1.242364{col 67}{space 3} .4157734
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 & sex==1 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     8,943
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       681
{txt}{col 49}{help j_robustsingular##|_new:F(  67,    270)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1575
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0801
{txt}{col 49}Root MSE{col 67}= {res}    0.4782

{txt}{ralign 78:(Std. Err. adjusted for {res:271} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0001979{col 26}{space 2} .0130472{col 37}{space 1}   -0.02{col 46}{space 3}0.988{col 54}{space 4} -.025885{col 67}{space 3} .0254893
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0237708{col 26}{space 2} .0031502{col 37}{space 1}    7.55{col 46}{space 3}0.000{col 54}{space 4} .0175688{col 67}{space 3} .0299728
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002634{col 26}{space 2} .0000356{col 37}{space 1}   -7.41{col 46}{space 3}0.000{col 54}{space 4}-.0003333{col 67}{space 3}-.0001934
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0135042{col 26}{space 2} .0206631{col 37}{space 1}    0.65{col 46}{space 3}0.514{col 54}{space 4}-.0271771{col 67}{space 3} .0541855
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0477366{col 26}{space 2} .0225486{col 37}{space 1}    2.12{col 46}{space 3}0.035{col 54}{space 4} .0033432{col 67}{space 3} .0921299
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0096295{col 26}{space 2}   .00805{col 37}{space 1}    1.20{col 46}{space 3}0.233{col 54}{space 4}-.0062192{col 67}{space 3} .0254782
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0873173{col 26}{space 2} .0156545{col 37}{space 1}    5.58{col 46}{space 3}0.000{col 54}{space 4} .0564969{col 67}{space 3} .1181378
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}4  {c |}{col 14}{res}{space 2} .0491262{col 26}{space 2} .0503345{col 37}{space 1}    0.98{col 46}{space 3}0.330{col 54}{space 4}-.0499718{col 67}{space 3} .1482243
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0359309{col 26}{space 2} .0989552{col 37}{space 1}    0.36{col 46}{space 3}0.717{col 54}{space 4}-.1588909{col 67}{space 3} .2307527
{txt}{space 10}8  {c |}{col 14}{res}{space 2} 1.379587{col 26}{space 2} .0781569{col 37}{space 1}   17.65{col 46}{space 3}0.000{col 54}{space 4} 1.225713{col 67}{space 3} 1.533461
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .4912724{col 26}{space 2} .1211011{col 37}{space 1}    4.06{col 46}{space 3}0.000{col 54}{space 4} .2528499{col 67}{space 3} .7296948
{txt}{space 9}10  {c |}{col 14}{res}{space 2}  .244223{col 26}{space 2} .1633899{col 37}{space 1}    1.49{col 46}{space 3}0.136{col 54}{space 4}-.0774571{col 67}{space 3} .5659032
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .9305543{col 26}{space 2} .1778552{col 37}{space 1}    5.23{col 46}{space 3}0.000{col 54}{space 4} .5803949{col 67}{space 3} 1.280714
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .554984{col 26}{space 2} .1005367{col 37}{space 1}    5.52{col 46}{space 3}0.000{col 54}{space 4} .3570484{col 67}{space 3} .7529196
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .6795267{col 26}{space 2} .1189107{col 37}{space 1}    5.71{col 46}{space 3}0.000{col 54}{space 4} .4454167{col 67}{space 3} .9136367
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .7205847{col 26}{space 2} .0775974{col 37}{space 1}    9.29{col 46}{space 3}0.000{col 54}{space 4} .5678118{col 67}{space 3} .8733576
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .4847548{col 26}{space 2} .1562127{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4}  .177205{col 67}{space 3} .7923045
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .7132056{col 26}{space 2}  .067459{col 37}{space 1}   10.57{col 46}{space 3}0.000{col 54}{space 4} .5803931{col 67}{space 3} .8460181
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .6332584{col 26}{space 2}  .064217{col 37}{space 1}    9.86{col 46}{space 3}0.000{col 54}{space 4} .5068286{col 67}{space 3} .7596881
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .6487763{col 26}{space 2} .0646773{col 37}{space 1}   10.03{col 46}{space 3}0.000{col 54}{space 4} .5214402{col 67}{space 3} .7761123
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .5962576{col 26}{space 2} .0901964{col 37}{space 1}    6.61{col 46}{space 3}0.000{col 54}{space 4}   .41868{col 67}{space 3} .7738353
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{txt}{space 9}45  {c |}{col 14}{res}{space 2} .4863042{col 26}{space 2} .0649077{col 37}{space 1}    7.49{col 46}{space 3}0.000{col 54}{space 4} .3585146{col 67}{space 3} .6140937
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .5542706{col 26}{space 2} .0857143{col 37}{space 1}    6.47{col 46}{space 3}0.000{col 54}{space 4} .3855172{col 67}{space 3}  .723024
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .5213399{col 26}{space 2} .0652501{col 37}{space 1}    7.99{col 46}{space 3}0.000{col 54}{space 4} .3928762{col 67}{space 3} .6498036
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .6087846{col 26}{space 2} .0585584{col 37}{space 1}   10.40{col 46}{space 3}0.000{col 54}{space 4} .4934955{col 67}{space 3} .7240737
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .5012436{col 26}{space 2} .0565614{col 37}{space 1}    8.86{col 46}{space 3}0.000{col 54}{space 4} .3898861{col 67}{space 3} .6126011
{txt}{space 9}50  {c |}{col 14}{res}{space 2}  .513747{col 26}{space 2} .0667396{col 37}{space 1}    7.70{col 46}{space 3}0.000{col 54}{space 4} .3823508{col 67}{space 3} .6451433
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .6044613{col 26}{space 2} .1740995{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4}  .261696{col 67}{space 3} .9472265
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5685167{col 26}{space 2} .1130531{col 37}{space 1}    5.03{col 46}{space 3}0.000{col 54}{space 4}  .345939{col 67}{space 3} .7910945
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .6289975{col 26}{space 2}  .090576{col 37}{space 1}    6.94{col 46}{space 3}0.000{col 54}{space 4} .4506725{col 67}{space 3} .8073225
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .5320188{col 26}{space 2} .1241821{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} .2875304{col 67}{space 3} .7765071
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .5226398{col 26}{space 2} .1045313{col 37}{space 1}    5.00{col 46}{space 3}0.000{col 54}{space 4} .3168397{col 67}{space 3} .7284398
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .4247378{col 26}{space 2} .2552231{col 37}{space 1}    1.66{col 46}{space 3}0.097{col 54}{space 4}-.0777427{col 67}{space 3} .9272184
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .2416128{col 26}{space 2} .1298182{col 37}{space 1}    1.86{col 46}{space 3}0.064{col 54}{space 4}-.0139717{col 67}{space 3} .4971974
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{txt}{space 9}59  {c |}{col 14}{res}{space 2} .2796384{col 26}{space 2} .1109284{col 37}{space 1}    2.52{col 46}{space 3}0.012{col 54}{space 4} .0612438{col 67}{space 3} .4980331
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .2935834{col 26}{space 2} .1091919{col 37}{space 1}    2.69{col 46}{space 3}0.008{col 54}{space 4} .0786076{col 67}{space 3} .5085592
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .4942658{col 26}{space 2} .1306558{col 37}{space 1}    3.78{col 46}{space 3}0.000{col 54}{space 4} .2370321{col 67}{space 3} .7514995
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .5278974{col 26}{space 2}  .104641{col 37}{space 1}    5.04{col 46}{space 3}0.000{col 54}{space 4} .3218813{col 67}{space 3} .7339135
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .4945193{col 26}{space 2} .1174427{col 37}{space 1}    4.21{col 46}{space 3}0.000{col 54}{space 4} .2632994{col 67}{space 3} .7257391
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .3829834{col 26}{space 2} .0770507{col 37}{space 1}    4.97{col 46}{space 3}0.000{col 54}{space 4} .2312869{col 67}{space 3}   .53468
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .3211479{col 26}{space 2} .0669763{col 37}{space 1}    4.79{col 46}{space 3}0.000{col 54}{space 4} .1892857{col 67}{space 3} .4530101
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .5052489{col 26}{space 2} .1778074{col 37}{space 1}    2.84{col 46}{space 3}0.005{col 54}{space 4} .1551836{col 67}{space 3} .8553141
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .6206618{col 26}{space 2} .1085416{col 37}{space 1}    5.72{col 46}{space 3}0.000{col 54}{space 4} .4069663{col 67}{space 3} .8343573
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .7224658{col 26}{space 2} .1129505{col 37}{space 1}    6.40{col 46}{space 3}0.000{col 54}{space 4} .5000901{col 67}{space 3} .9448415
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .6934295{col 26}{space 2} .1203883{col 37}{space 1}    5.76{col 46}{space 3}0.000{col 54}{space 4} .4564103{col 67}{space 3} .9304487
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .6213264{col 26}{space 2} .1006839{col 37}{space 1}    6.17{col 46}{space 3}0.000{col 54}{space 4} .4231011{col 67}{space 3} .8195517
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.5168845{col 26}{space 2} .0708005{col 37}{space 1}   -7.30{col 46}{space 3}0.000{col 54}{space 4}-.6562758{col 67}{space 3}-.3774932
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 & sex==1 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     8,408
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       386
{txt}{col 49}F({res}  71{txt},{res}    171{txt}){col 67}= {res}    142.80
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1379
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0884
{txt}{col 49}Root MSE{col 67}= {res}    0.4774

{txt}{ralign 78:(Std. Err. adjusted for {res:172} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0126062{col 26}{space 2} .0194009{col 37}{space 1}   -0.65{col 46}{space 3}0.517{col 54}{space 4}-.0509024{col 67}{space 3} .0256899
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0177608{col 26}{space 2} .0023204{col 37}{space 1}    7.65{col 46}{space 3}0.000{col 54}{space 4} .0131804{col 67}{space 3} .0223411
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001865{col 26}{space 2} .0000245{col 37}{space 1}   -7.60{col 46}{space 3}0.000{col 54}{space 4}-.0002349{col 67}{space 3} -.000138
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0155059{col 26}{space 2} .0149394{col 37}{space 1}   -1.04{col 46}{space 3}0.301{col 54}{space 4}-.0449954{col 67}{space 3} .0139835
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0316728{col 26}{space 2} .0220701{col 37}{space 1}    1.44{col 46}{space 3}0.153{col 54}{space 4}-.0118922{col 67}{space 3} .0752378
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0189268{col 26}{space 2} .0080104{col 37}{space 1}    2.36{col 46}{space 3}0.019{col 54}{space 4} .0031147{col 67}{space 3} .0347389
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2}  .083192{col 26}{space 2} .0122806{col 37}{space 1}    6.77{col 46}{space 3}0.000{col 54}{space 4} .0589509{col 67}{space 3} .1074332
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .3257782{col 26}{space 2} .1403475{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4} .0487415{col 67}{space 3}  .602815
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2211918{col 26}{space 2} .0807952{col 37}{space 1}    2.74{col 46}{space 3}0.007{col 54}{space 4} .0617073{col 67}{space 3} .3806763
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0857612{col 26}{space 2} .0573685{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4}-.0274804{col 67}{space 3} .1990028
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2354092{col 26}{space 2} .1084658{col 37}{space 1}    2.17{col 46}{space 3}0.031{col 54}{space 4} .0213049{col 67}{space 3} .4495135
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .7502569{col 26}{space 2} .1207967{col 37}{space 1}    6.21{col 46}{space 3}0.000{col 54}{space 4} .5118121{col 67}{space 3} .9887017
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .488166{col 26}{space 2} .1530622{col 37}{space 1}    3.19{col 46}{space 3}0.002{col 54}{space 4} .1860314{col 67}{space 3} .7903007
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5655884{col 26}{space 2} .1237233{col 37}{space 1}    4.57{col 46}{space 3}0.000{col 54}{space 4} .3213668{col 67}{space 3} .8098101
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .6452037{col 26}{space 2} .1393893{col 37}{space 1}    4.63{col 46}{space 3}0.000{col 54}{space 4} .3700584{col 67}{space 3}  .920349
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .3079386{col 26}{space 2} .1517896{col 37}{space 1}    2.03{col 46}{space 3}0.044{col 54}{space 4} .0083159{col 67}{space 3} .6075614
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .6150979{col 26}{space 2} .0589656{col 37}{space 1}   10.43{col 46}{space 3}0.000{col 54}{space 4} .4987037{col 67}{space 3} .7314921
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .5604996{col 26}{space 2} .0468044{col 37}{space 1}   11.98{col 46}{space 3}0.000{col 54}{space 4} .4681109{col 67}{space 3} .6528883
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5859328{col 26}{space 2} .0413784{col 37}{space 1}   14.16{col 46}{space 3}0.000{col 54}{space 4} .5042546{col 67}{space 3}  .667611
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .4525426{col 26}{space 2} .0593232{col 37}{space 1}    7.63{col 46}{space 3}0.000{col 54}{space 4} .3354425{col 67}{space 3} .5696427
{txt}{space 9}20  {c |}{col 14}{res}{space 2}  .609159{col 26}{space 2} .0937748{col 37}{space 1}    6.50{col 46}{space 3}0.000{col 54}{space 4} .4240538{col 67}{space 3} .7942642
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .4071824{col 26}{space 2} .0686946{col 37}{space 1}    5.93{col 46}{space 3}0.000{col 54}{space 4} .2715837{col 67}{space 3} .5427811
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .3673646{col 26}{space 2} .0937102{col 37}{space 1}    3.92{col 46}{space 3}0.000{col 54}{space 4} .1823868{col 67}{space 3} .5523424
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .5129563{col 26}{space 2} .1253677{col 37}{space 1}    4.09{col 46}{space 3}0.000{col 54}{space 4} .2654887{col 67}{space 3} .7604239
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .4450783{col 26}{space 2} .0942335{col 37}{space 1}    4.72{col 46}{space 3}0.000{col 54}{space 4} .2590675{col 67}{space 3}  .631089
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5510711{col 26}{space 2} .0884457{col 37}{space 1}    6.23{col 46}{space 3}0.000{col 54}{space 4} .3764851{col 67}{space 3}  .725657
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .5245697{col 26}{space 2}  .064062{col 37}{space 1}    8.19{col 46}{space 3}0.000{col 54}{space 4} .3981154{col 67}{space 3} .6510239
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .5553273{col 26}{space 2} .0607192{col 37}{space 1}    9.15{col 46}{space 3}0.000{col 54}{space 4} .4354716{col 67}{space 3} .6751831
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .4426831{col 26}{space 2} .0404903{col 37}{space 1}   10.93{col 46}{space 3}0.000{col 54}{space 4}  .362758{col 67}{space 3} .5226081
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .3687177{col 26}{space 2} .0440514{col 37}{space 1}    8.37{col 46}{space 3}0.000{col 54}{space 4} .2817631{col 67}{space 3} .4556724
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .3340892{col 26}{space 2} .0463158{col 37}{space 1}    7.21{col 46}{space 3}0.000{col 54}{space 4} .2426649{col 67}{space 3} .4255135
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .5324146{col 26}{space 2} .0529737{col 37}{space 1}   10.05{col 46}{space 3}0.000{col 54}{space 4}  .427848{col 67}{space 3} .6369812
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .5278267{col 26}{space 2} .0541871{col 37}{space 1}    9.74{col 46}{space 3}0.000{col 54}{space 4} .4208649{col 67}{space 3} .6347884
{txt}{space 9}33  {c |}{col 14}{res}{space 2}  .529303{col 26}{space 2} .0422985{col 37}{space 1}   12.51{col 46}{space 3}0.000{col 54}{space 4} .4458084{col 67}{space 3} .6127975
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .3891108{col 26}{space 2}  .036227{col 37}{space 1}   10.74{col 46}{space 3}0.000{col 54}{space 4} .3176012{col 67}{space 3} .4606205
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .4199649{col 26}{space 2} .0447933{col 37}{space 1}    9.38{col 46}{space 3}0.000{col 54}{space 4} .3315459{col 67}{space 3}  .508384
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .5409714{col 26}{space 2} .0834936{col 37}{space 1}    6.48{col 46}{space 3}0.000{col 54}{space 4} .3761605{col 67}{space 3} .7057823
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .0048498{col 26}{space 2} .0410044{col 37}{space 1}    0.12{col 46}{space 3}0.906{col 54}{space 4}-.0760901{col 67}{space 3} .0857898
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .6394555{col 26}{space 2} .0565847{col 37}{space 1}   11.30{col 46}{space 3}0.000{col 54}{space 4}  .527761{col 67}{space 3}   .75115
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{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5105153{col 26}{space 2} .0634089{col 37}{space 1}    8.05{col 46}{space 3}0.000{col 54}{space 4} .3853502{col 67}{space 3} .6356803
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{txt}{space 9}46  {c |}{col 14}{res}{space 2} .6063432{col 26}{space 2} .0767555{col 37}{space 1}    7.90{col 46}{space 3}0.000{col 54}{space 4} .4548329{col 67}{space 3} .7578534
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .5622425{col 26}{space 2} .0520461{col 37}{space 1}   10.80{col 46}{space 3}0.000{col 54}{space 4} .4595069{col 67}{space 3} .6649781
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .4709666{col 26}{space 2}  .059314{col 37}{space 1}    7.94{col 46}{space 3}0.000{col 54}{space 4} .3538847{col 67}{space 3} .5880485
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .3825172{col 26}{space 2} .0621342{col 37}{space 1}    6.16{col 46}{space 3}0.000{col 54}{space 4} .2598683{col 67}{space 3} .5051661
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .4974897{col 26}{space 2} .0725912{col 37}{space 1}    6.85{col 46}{space 3}0.000{col 54}{space 4} .3541995{col 67}{space 3} .6407799
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .6387779{col 26}{space 2} .1033891{col 37}{space 1}    6.18{col 46}{space 3}0.000{col 54}{space 4} .4346948{col 67}{space 3} .8428611
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .6278457{col 26}{space 2} .1018094{col 37}{space 1}    6.17{col 46}{space 3}0.000{col 54}{space 4} .4268807{col 67}{space 3} .8288107
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5160989{col 26}{space 2} .0696626{col 37}{space 1}    7.41{col 46}{space 3}0.000{col 54}{space 4} .3785894{col 67}{space 3} .6536083
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .4819907{col 26}{space 2} .0766196{col 37}{space 1}    6.29{col 46}{space 3}0.000{col 54}{space 4} .3307486{col 67}{space 3} .6332327
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .6178933{col 26}{space 2}  .086352{col 37}{space 1}    7.16{col 46}{space 3}0.000{col 54}{space 4} .4474402{col 67}{space 3} .7883464
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .1788824{col 26}{space 2} .0685354{col 37}{space 1}    2.61{col 46}{space 3}0.010{col 54}{space 4}  .043598{col 67}{space 3} .3141667
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .1425189{col 26}{space 2} .0626859{col 37}{space 1}    2.27{col 46}{space 3}0.024{col 54}{space 4} .0187811{col 67}{space 3} .2662566
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{txt}{space 9}59  {c |}{col 14}{res}{space 2} .1101555{col 26}{space 2} .0493318{col 37}{space 1}    2.23{col 46}{space 3}0.027{col 54}{space 4} .0127779{col 67}{space 3} .2075332
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .1655848{col 26}{space 2} .0558352{col 37}{space 1}    2.97{col 46}{space 3}0.003{col 54}{space 4} .0553698{col 67}{space 3} .2757998
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .4573966{col 26}{space 2} .0872287{col 37}{space 1}    5.24{col 46}{space 3}0.000{col 54}{space 4} .2852129{col 67}{space 3} .6295802
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .3538974{col 26}{space 2} .0646009{col 37}{space 1}    5.48{col 46}{space 3}0.000{col 54}{space 4} .2263795{col 67}{space 3} .4814152
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .4062211{col 26}{space 2} .0712339{col 37}{space 1}    5.70{col 46}{space 3}0.000{col 54}{space 4} .2656101{col 67}{space 3} .5468322
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .3088763{col 26}{space 2} .0738611{col 37}{space 1}    4.18{col 46}{space 3}0.000{col 54}{space 4} .1630794{col 67}{space 3} .4546733
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .2414848{col 26}{space 2} .0786378{col 37}{space 1}    3.07{col 46}{space 3}0.002{col 54}{space 4} .0862589{col 67}{space 3} .3967107
{txt}{space 9}66  {c |}{col 14}{res}{space 2}  .667894{col 26}{space 2}  .081293{col 37}{space 1}    8.22{col 46}{space 3}0.000{col 54}{space 4} .5074271{col 67}{space 3} .8283609
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .5163071{col 26}{space 2} .1411508{col 37}{space 1}    3.66{col 46}{space 3}0.000{col 54}{space 4} .2376847{col 67}{space 3} .7949294
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .5876974{col 26}{space 2} .0703961{col 37}{space 1}    8.35{col 46}{space 3}0.000{col 54}{space 4} .4487402{col 67}{space 3} .7266545
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .5051666{col 26}{space 2} .0660563{col 37}{space 1}    7.65{col 46}{space 3}0.000{col 54}{space 4} .3747759{col 67}{space 3} .6355573
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4642972{col 26}{space 2} .0744809{col 37}{space 1}    6.23{col 46}{space 3}0.000{col 54}{space 4} .3172768{col 67}{space 3} .6113175
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3550992{col 26}{space 2} .0617121{col 37}{space 1}   -5.75{col 46}{space 3}0.000{col 54}{space 4}-.4769148{col 67}{space 3}-.2332837
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=. & sex==1, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     8,460
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       632
{txt}{col 49}F({res}  76{txt},{res}    240{txt}){col 67}= {res}    129.96
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1688
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0930
{txt}{col 49}Root MSE{col 67}= {res}    0.4761

{txt}{ralign 78:(Std. Err. adjusted for {res:241} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}  .028146{col 26}{space 2} .0043089{col 37}{space 1}    6.53{col 46}{space 3}0.000{col 54}{space 4} .0196579{col 67}{space 3} .0366342
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .016713{col 26}{space 2} .0026919{col 37}{space 1}    6.21{col 46}{space 3}0.000{col 54}{space 4} .0114103{col 67}{space 3} .0220157
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001755{col 26}{space 2} .0000299{col 37}{space 1}   -5.86{col 46}{space 3}0.000{col 54}{space 4}-.0002345{col 67}{space 3}-.0001165
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0062312{col 26}{space 2} .0138753{col 37}{space 1}   -0.45{col 46}{space 3}0.654{col 54}{space 4}-.0335641{col 67}{space 3} .0211017
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .1059311{col 26}{space 2} .0271106{col 37}{space 1}    3.91{col 46}{space 3}0.000{col 54}{space 4}  .052526{col 67}{space 3} .1593362
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0109018{col 26}{space 2} .0103811{col 37}{space 1}    1.05{col 46}{space 3}0.295{col 54}{space 4}-.0095479{col 67}{space 3} .0313515
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0750034{col 26}{space 2} .0095494{col 37}{space 1}    7.85{col 46}{space 3}0.000{col 54}{space 4} .0561922{col 67}{space 3} .0938147
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .6062391{col 26}{space 2} .4117071{col 37}{space 1}    1.47{col 46}{space 3}0.142{col 54}{space 4}-.2047817{col 67}{space 3}  1.41726
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .5326063{col 26}{space 2} .3109429{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4}-.0799193{col 67}{space 3} 1.145132
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4722887{col 26}{space 2} .3766296{col 37}{space 1}    1.25{col 46}{space 3}0.211{col 54}{space 4}-.2696331{col 67}{space 3}  1.21421
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1634034{col 26}{space 2} .2372895{col 37}{space 1}    0.69{col 46}{space 3}0.492{col 54}{space 4}-.3040325{col 67}{space 3} .6308393
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2752109{col 26}{space 2}   .10094{col 37}{space 1}    2.73{col 46}{space 3}0.007{col 54}{space 4} .0763694{col 67}{space 3} .4740524
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .3165385{col 26}{space 2}  .104283{col 37}{space 1}    3.04{col 46}{space 3}0.003{col 54}{space 4} .1111116{col 67}{space 3} .5219654
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2467187{col 26}{space 2} .1091596{col 37}{space 1}    2.26{col 46}{space 3}0.025{col 54}{space 4} .0316855{col 67}{space 3} .4617519
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2441126{col 26}{space 2} .0946448{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4}  .057672{col 67}{space 3} .4305532
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2488754{col 26}{space 2}  .097595{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} .0566232{col 67}{space 3} .4411275
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .5338913{col 26}{space 2} .1512683{col 37}{space 1}    3.53{col 46}{space 3}0.000{col 54}{space 4} .2359082{col 67}{space 3} .8318745
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .4399259{col 26}{space 2} .1288264{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4}  .186151{col 67}{space 3} .6937008
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .4884878{col 26}{space 2} .1356951{col 37}{space 1}    3.60{col 46}{space 3}0.000{col 54}{space 4} .2211824{col 67}{space 3} .7557931
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5515126{col 26}{space 2} .1413952{col 37}{space 1}    3.90{col 46}{space 3}0.000{col 54}{space 4} .2729786{col 67}{space 3} .8300466
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5323646{col 26}{space 2} .1246958{col 37}{space 1}    4.27{col 46}{space 3}0.000{col 54}{space 4} .2867267{col 67}{space 3} .7780025
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .5891637{col 26}{space 2} .0946663{col 37}{space 1}    6.22{col 46}{space 3}0.000{col 54}{space 4} .4026808{col 67}{space 3} .7756467
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .6005235{col 26}{space 2} .0917647{col 37}{space 1}    6.54{col 46}{space 3}0.000{col 54}{space 4} .4197565{col 67}{space 3} .7812906
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5851312{col 26}{space 2} .0921333{col 37}{space 1}    6.35{col 46}{space 3}0.000{col 54}{space 4}  .403638{col 67}{space 3} .7666243
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .4898552{col 26}{space 2}  .104748{col 37}{space 1}    4.68{col 46}{space 3}0.000{col 54}{space 4} .2835124{col 67}{space 3} .6961981
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .6607428{col 26}{space 2} .1191521{col 37}{space 1}    5.55{col 46}{space 3}0.000{col 54}{space 4} .4260254{col 67}{space 3} .8954602
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .5523208{col 26}{space 2}  .096722{col 37}{space 1}    5.71{col 46}{space 3}0.000{col 54}{space 4} .3617884{col 67}{space 3} .7428532
{txt}{space 9}22  {c |}{col 14}{res}{space 2}  .453955{col 26}{space 2} .0982318{col 37}{space 1}    4.62{col 46}{space 3}0.000{col 54}{space 4} .2604485{col 67}{space 3} .6474616
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .5474141{col 26}{space 2} .0958615{col 37}{space 1}    5.71{col 46}{space 3}0.000{col 54}{space 4} .3585768{col 67}{space 3} .7362515
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .5562701{col 26}{space 2} .0960993{col 37}{space 1}    5.79{col 46}{space 3}0.000{col 54}{space 4} .3669644{col 67}{space 3} .7455759
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5416262{col 26}{space 2} .0976664{col 37}{space 1}    5.55{col 46}{space 3}0.000{col 54}{space 4} .3492334{col 67}{space 3}  .734019
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .5290422{col 26}{space 2} .0900455{col 37}{space 1}    5.88{col 46}{space 3}0.000{col 54}{space 4} .3516617{col 67}{space 3} .7064226
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .4972676{col 26}{space 2} .1151968{col 37}{space 1}    4.32{col 46}{space 3}0.000{col 54}{space 4} .2703418{col 67}{space 3} .7241934
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .4040066{col 26}{space 2} .1167668{col 37}{space 1}    3.46{col 46}{space 3}0.001{col 54}{space 4}  .173988{col 67}{space 3} .6340253
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .4143613{col 26}{space 2} .1194629{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4} .1790315{col 67}{space 3}  .649691
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .2519918{col 26}{space 2} .1405174{col 37}{space 1}    1.79{col 46}{space 3}0.074{col 54}{space 4} -.024813{col 67}{space 3} .5287967
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .4814664{col 26}{space 2} .0923765{col 37}{space 1}    5.21{col 46}{space 3}0.000{col 54}{space 4} .2994941{col 67}{space 3} .6634387
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{txt}{space 9}33  {c |}{col 14}{res}{space 2} .4507138{col 26}{space 2} .0925083{col 37}{space 1}    4.87{col 46}{space 3}0.000{col 54}{space 4} .2684819{col 67}{space 3} .6329457
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .4002923{col 26}{space 2} .0908912{col 37}{space 1}    4.40{col 46}{space 3}0.000{col 54}{space 4} .2212459{col 67}{space 3} .5793387
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .4069653{col 26}{space 2} .0969444{col 37}{space 1}    4.20{col 46}{space 3}0.000{col 54}{space 4} .2159948{col 67}{space 3} .5979358
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{txt}{space 9}37  {c |}{col 14}{res}{space 2} .4960719{col 26}{space 2} .1054889{col 37}{space 1}    4.70{col 46}{space 3}0.000{col 54}{space 4} .2882695{col 67}{space 3} .7038743
{txt}{space 9}38  {c |}{col 14}{res}{space 2}  .531725{col 26}{space 2} .1015665{col 37}{space 1}    5.24{col 46}{space 3}0.000{col 54}{space 4} .3316494{col 67}{space 3} .7318005
{txt}{space 9}39  {c |}{col 14}{res}{space 2}  .445742{col 26}{space 2} .0953805{col 37}{space 1}    4.67{col 46}{space 3}0.000{col 54}{space 4} .2578521{col 67}{space 3} .6336319
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .4504554{col 26}{space 2} .1017594{col 37}{space 1}    4.43{col 46}{space 3}0.000{col 54}{space 4} .2499998{col 67}{space 3} .6509111
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .4067716{col 26}{space 2} .1263793{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .1578173{col 67}{space 3}  .655726
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .3053277{col 26}{space 2} .1168407{col 37}{space 1}    2.61{col 46}{space 3}0.010{col 54}{space 4} .0751635{col 67}{space 3} .5354919
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .6542859{col 26}{space 2} .1738969{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} .3117268{col 67}{space 3} .9968451
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .7151169{col 26}{space 2} .1915941{col 37}{space 1}    3.73{col 46}{space 3}0.000{col 54}{space 4} .3376962{col 67}{space 3} 1.092538
{txt}{space 9}45  {c |}{col 14}{res}{space 2}-.0157147{col 26}{space 2} .3593797{col 37}{space 1}   -0.04{col 46}{space 3}0.965{col 54}{space 4}-.7236559{col 67}{space 3} .6922266
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{txt}{space 9}47  {c |}{col 14}{res}{space 2} .5327837{col 26}{space 2} .1263076{col 37}{space 1}    4.22{col 46}{space 3}0.000{col 54}{space 4} .2839707{col 67}{space 3} .7815968
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .5061992{col 26}{space 2} .1253251{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4} .2593215{col 67}{space 3} .7530768
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .4319125{col 26}{space 2} .1392875{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4} .1575303{col 67}{space 3} .7062947
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .4575135{col 26}{space 2} .1476331{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4} .1666914{col 67}{space 3} .7483356
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .5290178{col 26}{space 2} .0846265{col 37}{space 1}    6.25{col 46}{space 3}0.000{col 54}{space 4} .3623122{col 67}{space 3} .6957234
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .4943039{col 26}{space 2} .0951339{col 37}{space 1}    5.20{col 46}{space 3}0.000{col 54}{space 4} .3068999{col 67}{space 3} .6817079
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .4622031{col 26}{space 2} .0921976{col 37}{space 1}    5.01{col 46}{space 3}0.000{col 54}{space 4} .2805832{col 67}{space 3}  .643823
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .4044811{col 26}{space 2} .1057985{col 37}{space 1}    3.82{col 46}{space 3}0.000{col 54}{space 4} .1960689{col 67}{space 3} .6128933
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3478497{col 26}{space 2} .0955426{col 37}{space 1}   -3.64{col 46}{space 3}0.000{col 54}{space 4}-.5360588{col 67}{space 3}-.1596406
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_men.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_men.tex"'})

{com}. 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if sex==0, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    25,711
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       944
{txt}{col 49}{help j_robustsingular##|_new:F(  75,    284)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1375
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1019
{txt}{col 49}Root MSE{col 67}= {res}    0.4738

{txt}{ralign 78:(Std. Err. adjusted for {res:285} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
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{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .1447803{col 26}{space 2} .2309221{col 37}{space 1}    0.63{col 46}{space 3}0.531{col 54}{space 4}-.3097557{col 67}{space 3} .5993163
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum<0.25 & sex==0, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 67.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     5,699
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       718
{txt}{col 49}{help j_robustsingular##|_new:F(  69,    274)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.2425
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1210
{txt}{col 49}Root MSE{col 67}= {res}    0.4667

{txt}{ralign 78:(Std. Err. adjusted for {res:275} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0153434{col 26}{space 2} .0243975{col 37}{space 1}    0.63{col 46}{space 3}0.530{col 54}{space 4} -.032687{col 67}{space 3} .0633738
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0252998{col 26}{space 2} .0031462{col 37}{space 1}    8.04{col 46}{space 3}0.000{col 54}{space 4}  .019106{col 67}{space 3} .0314936
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0003221{col 26}{space 2} .0000371{col 37}{space 1}   -8.68{col 46}{space 3}0.000{col 54}{space 4}-.0003952{col 67}{space 3}-.0002491
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0218812{col 26}{space 2} .0191146{col 37}{space 1}    1.14{col 46}{space 3}0.253{col 54}{space 4}-.0157489{col 67}{space 3} .0595114
{txt}{space 6}parent {c |}{col 14}{res}{space 2}  .063905{col 26}{space 2} .0239072{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4}   .01684{col 67}{space 3} .1109701
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0111007{col 26}{space 2} .0072693{col 37}{space 1}    1.53{col 46}{space 3}0.128{col 54}{space 4}  -.00321{col 67}{space 3} .0254115
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0553444{col 26}{space 2} .0196414{col 37}{space 1}    2.82{col 46}{space 3}0.005{col 54}{space 4} .0166773{col 67}{space 3} .0940116
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.1949864{col 26}{space 2}  .056713{col 37}{space 1}   -3.44{col 46}{space 3}0.001{col 54}{space 4}-.3066349{col 67}{space 3}-.0833379
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .0174822{col 26}{space 2} .0632187{col 37}{space 1}    0.28{col 46}{space 3}0.782{col 54}{space 4}-.1069739{col 67}{space 3} .1419383
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .0301181{col 26}{space 2} .0548112{col 37}{space 1}    0.55{col 46}{space 3}0.583{col 54}{space 4}-.0777864{col 67}{space 3} .1380227
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2852574{col 26}{space 2} .1421474{col 37}{space 1}    2.01{col 46}{space 3}0.046{col 54}{space 4} .0054176{col 67}{space 3} .5650971
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0127961{col 26}{space 2} .1022025{col 37}{space 1}    0.13{col 46}{space 3}0.900{col 54}{space 4}-.1884058{col 67}{space 3} .2139979
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .6034639{col 26}{space 2} .1101644{col 37}{space 1}    5.48{col 46}{space 3}0.000{col 54}{space 4} .3865878{col 67}{space 3} .8203401
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5489223{col 26}{space 2} .1193468{col 37}{space 1}    4.60{col 46}{space 3}0.000{col 54}{space 4} .3139691{col 67}{space 3} .7838755
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .5419388{col 26}{space 2} .0820775{col 37}{space 1}    6.60{col 46}{space 3}0.000{col 54}{space 4} .3803561{col 67}{space 3} .7035216
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .4651282{col 26}{space 2} .0935218{col 37}{space 1}    4.97{col 46}{space 3}0.000{col 54}{space 4} .2810156{col 67}{space 3} .6492407
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5808107{col 26}{space 2} .0993627{col 37}{space 1}    5.85{col 46}{space 3}0.000{col 54}{space 4} .3851994{col 67}{space 3}  .776422
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .6395393{col 26}{space 2} .0598098{col 37}{space 1}   10.69{col 46}{space 3}0.000{col 54}{space 4} .5217943{col 67}{space 3} .7572844
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .5929734{col 26}{space 2} .0658723{col 37}{space 1}    9.00{col 46}{space 3}0.000{col 54}{space 4} .4632933{col 67}{space 3} .7226536
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5083104{col 26}{space 2} .0538271{col 37}{space 1}    9.44{col 46}{space 3}0.000{col 54}{space 4} .4023431{col 67}{space 3} .6142777
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .5064696{col 26}{space 2} .0809371{col 37}{space 1}    6.26{col 46}{space 3}0.000{col 54}{space 4} .3471319{col 67}{space 3} .6658073
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .1852298{col 26}{space 2} .1370731{col 37}{space 1}    1.35{col 46}{space 3}0.178{col 54}{space 4}-.0846204{col 67}{space 3}   .45508
{txt}{space 9}21  {c |}{col 14}{res}{space 2}  .254133{col 26}{space 2} .1613699{col 37}{space 1}    1.57{col 46}{space 3}0.116{col 54}{space 4}-.0635495{col 67}{space 3} .5718154
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .4018353{col 26}{space 2} .2382157{col 37}{space 1}    1.69{col 46}{space 3}0.093{col 54}{space 4}-.0671304{col 67}{space 3} .8708009
{txt}{space 9}23  {c |}{col 14}{res}{space 2}  .284114{col 26}{space 2}  .144626{col 37}{space 1}    1.96{col 46}{space 3}0.050{col 54}{space 4}-.0006054{col 67}{space 3} .5688335
{txt}{space 9}24  {c |}{col 14}{res}{space 2}  .250887{col 26}{space 2} .1584494{col 37}{space 1}    1.58{col 46}{space 3}0.114{col 54}{space 4}-.0610459{col 67}{space 3} .5628199
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5733682{col 26}{space 2}  .152909{col 37}{space 1}    3.75{col 46}{space 3}0.000{col 54}{space 4} .2723423{col 67}{space 3}  .874394
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .6396051{col 26}{space 2} .0746625{col 37}{space 1}    8.57{col 46}{space 3}0.000{col 54}{space 4}   .49262{col 67}{space 3} .7865902
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .4801381{col 26}{space 2} .0635382{col 37}{space 1}    7.56{col 46}{space 3}0.000{col 54}{space 4} .3550531{col 67}{space 3} .6052231
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .3606448{col 26}{space 2} .0606976{col 37}{space 1}    5.94{col 46}{space 3}0.000{col 54}{space 4} .2411518{col 67}{space 3} .4801377
{txt}{space 9}29  {c |}{col 14}{res}{space 2}  .368027{col 26}{space 2} .0665954{col 37}{space 1}    5.53{col 46}{space 3}0.000{col 54}{space 4} .2369234{col 67}{space 3} .4991307
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .2819313{col 26}{space 2} .0553872{col 37}{space 1}    5.09{col 46}{space 3}0.000{col 54}{space 4} .1728928{col 67}{space 3} .3909698
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .4878518{col 26}{space 2} .1072845{col 37}{space 1}    4.55{col 46}{space 3}0.000{col 54}{space 4} .2766451{col 67}{space 3} .6990586
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .4858115{col 26}{space 2} .0917888{col 37}{space 1}    5.29{col 46}{space 3}0.000{col 54}{space 4} .3051106{col 67}{space 3} .6665123
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .4043951{col 26}{space 2} .0739848{col 37}{space 1}    5.47{col 46}{space 3}0.000{col 54}{space 4} .2587441{col 67}{space 3}  .550046
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .2914429{col 26}{space 2} .0637721{col 37}{space 1}    4.57{col 46}{space 3}0.000{col 54}{space 4} .1658974{col 67}{space 3} .4169884
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .2506039{col 26}{space 2} .0803431{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .0924357{col 67}{space 3} .4087722
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .2733497{col 26}{space 2} .2922936{col 37}{space 1}    0.94{col 46}{space 3}0.351{col 54}{space 4} -.302077{col 67}{space 3} .8487763
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .8138666{col 26}{space 2} .0819147{col 37}{space 1}    9.94{col 46}{space 3}0.000{col 54}{space 4} .6526044{col 67}{space 3} .9751289
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .4558241{col 26}{space 2} .2060775{col 37}{space 1}    2.21{col 46}{space 3}0.028{col 54}{space 4} .0501278{col 67}{space 3} .8615205
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .4452166{col 26}{space 2} .3064734{col 37}{space 1}    1.45{col 46}{space 3}0.147{col 54}{space 4}-.1581251{col 67}{space 3} 1.048558
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5727378{col 26}{space 2} .1279922{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .3207648{col 67}{space 3} .8247108
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .4806435{col 26}{space 2} .0457416{col 37}{space 1}   10.51{col 46}{space 3}0.000{col 54}{space 4} .3905938{col 67}{space 3} .5706933
{txt}{space 9}42  {c |}{col 14}{res}{space 2}  .516016{col 26}{space 2} .0488407{col 37}{space 1}   10.57{col 46}{space 3}0.000{col 54}{space 4} .4198653{col 67}{space 3} .6121667
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .4123815{col 26}{space 2} .0529828{col 37}{space 1}    7.78{col 46}{space 3}0.000{col 54}{space 4} .3080765{col 67}{space 3} .5166865
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .3796107{col 26}{space 2} .0559006{col 37}{space 1}    6.79{col 46}{space 3}0.000{col 54}{space 4} .2695615{col 67}{space 3} .4896599
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .3766912{col 26}{space 2}  .057709{col 37}{space 1}    6.53{col 46}{space 3}0.000{col 54}{space 4} .2630817{col 67}{space 3} .4903007
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .4073314{col 26}{space 2}  .117202{col 37}{space 1}    3.48{col 46}{space 3}0.001{col 54}{space 4} .1766005{col 67}{space 3} .6380624
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .4580023{col 26}{space 2} .0745785{col 37}{space 1}    6.14{col 46}{space 3}0.000{col 54}{space 4} .3111827{col 67}{space 3} .6048219
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .4946001{col 26}{space 2}  .079072{col 37}{space 1}    6.26{col 46}{space 3}0.000{col 54}{space 4} .3389342{col 67}{space 3} .6502659
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .4295193{col 26}{space 2} .0563715{col 37}{space 1}    7.62{col 46}{space 3}0.000{col 54}{space 4} .3185431{col 67}{space 3} .5404956
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .3015773{col 26}{space 2} .0396099{col 37}{space 1}    7.61{col 46}{space 3}0.000{col 54}{space 4} .2235988{col 67}{space 3} .3795558
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .6229939{col 26}{space 2} .1309847{col 37}{space 1}    4.76{col 46}{space 3}0.000{col 54}{space 4} .3651296{col 67}{space 3} .8808582
{txt}{space 9}52  {c |}{col 14}{res}{space 2}  .450465{col 26}{space 2}  .100803{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .2520182{col 67}{space 3} .6489118
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .5666305{col 26}{space 2} .1279561{col 37}{space 1}    4.43{col 46}{space 3}0.000{col 54}{space 4} .3147284{col 67}{space 3} .8185326
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .3549561{col 26}{space 2} .1527928{col 37}{space 1}    2.32{col 46}{space 3}0.021{col 54}{space 4} .0541592{col 67}{space 3}  .655753
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .1947144{col 26}{space 2} .0922961{col 37}{space 1}    2.11{col 46}{space 3}0.036{col 54}{space 4} .0130148{col 67}{space 3} .3764141
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .1758512{col 26}{space 2} .0894964{col 37}{space 1}    1.96{col 46}{space 3}0.050{col 54}{space 4}-.0003367{col 67}{space 3} .3520392
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .4002052{col 26}{space 2} .1432544{col 37}{space 1}    2.79{col 46}{space 3}0.006{col 54}{space 4}  .118186{col 67}{space 3} .6822243
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .2472067{col 26}{space 2} .0896671{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} .0706826{col 67}{space 3} .4237308
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .2377604{col 26}{space 2} .2044504{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4} -.164733{col 67}{space 3} .6402537
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .0638824{col 26}{space 2} .1548124{col 37}{space 1}    0.41{col 46}{space 3}0.680{col 54}{space 4}-.2408905{col 67}{space 3} .3686552
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .5286795{col 26}{space 2} .1412271{col 37}{space 1}    3.74{col 46}{space 3}0.000{col 54}{space 4} .2506514{col 67}{space 3} .8067076
{txt}{space 9}62  {c |}{col 14}{res}{space 2}  .496443{col 26}{space 2} .1069667{col 37}{space 1}    4.64{col 46}{space 3}0.000{col 54}{space 4} .2858619{col 67}{space 3} .7070241
{txt}{space 9}63  {c |}{col 14}{res}{space 2}  .258165{col 26}{space 2} .0986035{col 37}{space 1}    2.62{col 46}{space 3}0.009{col 54}{space 4} .0640482{col 67}{space 3} .4522818
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .3023738{col 26}{space 2} .0795547{col 37}{space 1}    3.80{col 46}{space 3}0.000{col 54}{space 4} .1457576{col 67}{space 3} .4589899
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .2352415{col 26}{space 2} .0915704{col 37}{space 1}    2.57{col 46}{space 3}0.011{col 54}{space 4} .0549706{col 67}{space 3} .4155124
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .7854878{col 26}{space 2} .1882392{col 37}{space 1}    4.17{col 46}{space 3}0.000{col 54}{space 4} .4149089{col 67}{space 3} 1.156067
{txt}{space 9}67  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 9}68  {c |}{col 14}{res}{space 2} .6027533{col 26}{space 2} .1656692{col 37}{space 1}    3.64{col 46}{space 3}0.000{col 54}{space 4}  .276607{col 67}{space 3} .9288996
{txt}{space 9}69  {c |}{col 14}{res}{space 2}  .520016{col 26}{space 2} .1301256{col 37}{space 1}    4.00{col 46}{space 3}0.000{col 54}{space 4}  .263843{col 67}{space 3}  .776189
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4122471{col 26}{space 2} .1336028{col 37}{space 1}    3.09{col 46}{space 3}0.002{col 54}{space 4} .1492287{col 67}{space 3} .6752655
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3982114{col 26}{space 2} .0547397{col 37}{space 1}   -7.27{col 46}{space 3}0.000{col 54}{space 4}-.5059752{col 67}{space 3}-.2904476
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 & sex==0 , absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{txt}note: 21.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     6,318
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       633
{txt}{col 49}{help j_robustsingular##|_new:F(  67,    270)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.2024
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1023
{txt}{col 49}Root MSE{col 67}= {res}    0.4734

{txt}{ralign 78:(Std. Err. adjusted for {res:271} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0314131{col 26}{space 2} .0149555{col 37}{space 1}   -2.10{col 46}{space 3}0.037{col 54}{space 4}-.0608574{col 67}{space 3}-.0019688
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0174538{col 26}{space 2}  .003395{col 37}{space 1}    5.14{col 46}{space 3}0.000{col 54}{space 4} .0107699{col 67}{space 3} .0241378
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002375{col 26}{space 2} .0000365{col 37}{space 1}   -6.51{col 46}{space 3}0.000{col 54}{space 4}-.0003094{col 67}{space 3}-.0001657
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0440221{col 26}{space 2} .0179568{col 37}{space 1}    2.45{col 46}{space 3}0.015{col 54}{space 4} .0086691{col 67}{space 3} .0793752
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0319345{col 26}{space 2} .0238603{col 37}{space 1}    1.34{col 46}{space 3}0.182{col 54}{space 4}-.0150415{col 67}{space 3} .0789104
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0176971{col 26}{space 2} .0094167{col 37}{space 1}    1.88{col 46}{space 3}0.061{col 54}{space 4}-.0008423{col 67}{space 3} .0362366
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0651526{col 26}{space 2} .0155678{col 37}{space 1}    4.19{col 46}{space 3}0.000{col 54}{space 4} .0345029{col 67}{space 3} .0958022
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.3512649{col 26}{space 2} .0493245{col 37}{space 1}   -7.12{col 46}{space 3}0.000{col 54}{space 4}-.4483744{col 67}{space 3}-.2541554
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.2196359{col 26}{space 2} .0542186{col 37}{space 1}   -4.05{col 46}{space 3}0.000{col 54}{space 4}-.3263809{col 67}{space 3} -.112891
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1884826{col 26}{space 2} .0364445{col 37}{space 1}   -5.17{col 46}{space 3}0.000{col 54}{space 4}-.2602342{col 67}{space 3}-.1167311
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2103591{col 26}{space 2} .0736689{col 37}{space 1}    2.86{col 46}{space 3}0.005{col 54}{space 4} .0653206{col 67}{space 3} .3553977
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2} .4174053{col 26}{space 2}  .230018{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0354516{col 67}{space 3} .8702621
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .2143222{col 26}{space 2} .1206545{col 37}{space 1}    1.78{col 46}{space 3}0.077{col 54}{space 4} -.023221{col 67}{space 3} .4518654
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0770586{col 26}{space 2} .1841843{col 37}{space 1}    0.42{col 46}{space 3}0.676{col 54}{space 4}-.2855614{col 67}{space 3} .4396785
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .6933281{col 26}{space 2}  .148588{col 37}{space 1}    4.67{col 46}{space 3}0.000{col 54}{space 4} .4007896{col 67}{space 3} .9858665
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2182605{col 26}{space 2} .0591718{col 37}{space 1}   -3.69{col 46}{space 3}0.000{col 54}{space 4}-.3347572{col 67}{space 3}-.1017638
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 & sex==0 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     6,853
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       383
{txt}{col 49}{help j_robustsingular##|_new:F(  70,    166)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1552
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0954
{txt}{col 49}Root MSE{col 67}= {res}    0.4753

{txt}{ralign 78:(Std. Err. adjusted for {res:167} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0057919{col 26}{space 2} .0179415{col 37}{space 1}    0.32{col 46}{space 3}0.747{col 54}{space 4}-.0296311{col 67}{space 3} .0412149
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0203169{col 26}{space 2}   .00256{col 37}{space 1}    7.94{col 46}{space 3}0.000{col 54}{space 4} .0152624{col 67}{space 3} .0253713
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002416{col 26}{space 2} .0000303{col 37}{space 1}   -7.98{col 46}{space 3}0.000{col 54}{space 4}-.0003014{col 67}{space 3}-.0001818
{txt}{space 5}married {c |}{col 14}{res}{space 2}  .011245{col 26}{space 2} .0151592{col 37}{space 1}    0.74{col 46}{space 3}0.459{col 54}{space 4}-.0186848{col 67}{space 3} .0411748
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0407753{col 26}{space 2} .0190374{col 37}{space 1}    2.14{col 46}{space 3}0.034{col 54}{space 4} .0031887{col 67}{space 3} .0783619
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0136112{col 26}{space 2} .0065021{col 37}{space 1}    2.09{col 46}{space 3}0.038{col 54}{space 4} .0007737{col 67}{space 3} .0264487
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0917647{col 26}{space 2} .0194186{col 37}{space 1}    4.73{col 46}{space 3}0.000{col 54}{space 4} .0534254{col 67}{space 3} .1301039
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2}-.4242524{col 26}{space 2} .2034056{col 37}{space 1}   -2.09{col 46}{space 3}0.039{col 54}{space 4}-.8258479{col 67}{space 3} -.022657
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{txt}{space 9}10  {c |}{col 14}{res}{space 2} -.413909{col 26}{space 2} .2005364{col 37}{space 1}   -2.06{col 46}{space 3}0.041{col 54}{space 4}-.8098395{col 67}{space 3}-.0179784
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.2309798{col 26}{space 2}  .119939{col 37}{space 1}   -1.93{col 46}{space 3}0.056{col 54}{space 4}-.4677823{col 67}{space 3} .0058228
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.1923128{col 26}{space 2} .1350737{col 37}{space 1}   -1.42{col 46}{space 3}0.156{col 54}{space 4}-.4589965{col 67}{space 3}  .074371
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.5322937{col 26}{space 2}  .156271{col 37}{space 1}   -3.41{col 46}{space 3}0.001{col 54}{space 4}-.8408286{col 67}{space 3}-.2237588
{txt}{space 9}14  {c |}{col 14}{res}{space 2}-.1605219{col 26}{space 2} .1711647{col 37}{space 1}   -0.94{col 46}{space 3}0.350{col 54}{space 4}-.4984622{col 67}{space 3} .1774184
{txt}{space 9}15  {c |}{col 14}{res}{space 2} -.168636{col 26}{space 2}  .162047{col 37}{space 1}   -1.04{col 46}{space 3}0.300{col 54}{space 4}-.4885748{col 67}{space 3} .1513027
{txt}{space 9}16  {c |}{col 14}{res}{space 2} -.042576{col 26}{space 2} .0912541{col 37}{space 1}   -0.47{col 46}{space 3}0.641{col 54}{space 4}-.2227443{col 67}{space 3} .1375924
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-.0471696{col 26}{space 2} .1011181{col 37}{space 1}   -0.47{col 46}{space 3}0.641{col 54}{space 4} -.246813{col 67}{space 3} .1524737
{txt}{space 9}18  {c |}{col 14}{res}{space 2}-.0705021{col 26}{space 2}  .097583{col 37}{space 1}   -0.72{col 46}{space 3}0.471{col 54}{space 4} -.263166{col 67}{space 3} .1221617
{txt}{space 9}19  {c |}{col 14}{res}{space 2}-.1747859{col 26}{space 2} .1154068{col 37}{space 1}   -1.51{col 46}{space 3}0.132{col 54}{space 4}-.4026401{col 67}{space 3} .0530683
{txt}{space 9}20  {c |}{col 14}{res}{space 2}-.2082086{col 26}{space 2} .1263017{col 37}{space 1}   -1.65{col 46}{space 3}0.101{col 54}{space 4}-.4575733{col 67}{space 3} .0411562
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.1882379{col 26}{space 2} .1039188{col 37}{space 1}   -1.81{col 46}{space 3}0.072{col 54}{space 4}-.3934109{col 67}{space 3}  .016935
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.3595999{col 26}{space 2} .1634341{col 37}{space 1}   -2.20{col 46}{space 3}0.029{col 54}{space 4}-.6822773{col 67}{space 3}-.0369225
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.0722476{col 26}{space 2} .1340072{col 37}{space 1}   -0.54{col 46}{space 3}0.591{col 54}{space 4}-.3368258{col 67}{space 3} .1923306
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.1005951{col 26}{space 2} .1775245{col 37}{space 1}   -0.57{col 46}{space 3}0.572{col 54}{space 4} -.451092{col 67}{space 3} .2499019
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-.2220302{col 26}{space 2}  .103745{col 37}{space 1}   -2.14{col 46}{space 3}0.034{col 54}{space 4}-.4268599{col 67}{space 3}-.0172006
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{txt}{space 9}27  {c |}{col 14}{res}{space 2}-.2032597{col 26}{space 2} .0929485{col 37}{space 1}   -2.19{col 46}{space 3}0.030{col 54}{space 4}-.3867733{col 67}{space 3} -.019746
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.2430013{col 26}{space 2} .0969332{col 37}{space 1}   -2.51{col 46}{space 3}0.013{col 54}{space 4}-.4343822{col 67}{space 3}-.0516204
{txt}{space 9}29  {c |}{col 14}{res}{space 2}-.3168704{col 26}{space 2} .0932873{col 37}{space 1}   -3.40{col 46}{space 3}0.001{col 54}{space 4}-.5010529{col 67}{space 3}-.1326878
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-.3877791{col 26}{space 2} .1022774{col 37}{space 1}   -3.79{col 46}{space 3}0.000{col 54}{space 4}-.5897113{col 67}{space 3}-.1858469
{txt}{space 9}31  {c |}{col 14}{res}{space 2}-.1439356{col 26}{space 2} .1376571{col 37}{space 1}   -1.05{col 46}{space 3}0.297{col 54}{space 4}  -.41572{col 67}{space 3} .1278488
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .2965842{col 26}{space 2} .1000508{col 37}{space 1}    2.96{col 46}{space 3}0.003{col 54}{space 4} .0990482{col 67}{space 3} .4941202
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=. & sex==0, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     6,841
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       557
{txt}{col 49}{help j_robustsingular##|_new:F(  74,    212)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1883
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1058
{txt}{col 49}Root MSE{col 67}= {res}    0.4726

{txt}{ralign 78:(Std. Err. adjusted for {res:213} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0156129{col 26}{space 2} .0124386{col 37}{space 1}    1.26{col 46}{space 3}0.211{col 54}{space 4}-.0089064{col 67}{space 3} .0401322
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0140293{col 26}{space 2} .0024077{col 37}{space 1}    5.83{col 46}{space 3}0.000{col 54}{space 4} .0092831{col 67}{space 3} .0187755
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001767{col 26}{space 2} .0000266{col 37}{space 1}   -6.64{col 46}{space 3}0.000{col 54}{space 4}-.0002291{col 67}{space 3}-.0001242
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0202254{col 26}{space 2} .0135542{col 37}{space 1}    1.49{col 46}{space 3}0.137{col 54}{space 4}-.0064929{col 67}{space 3} .0469436
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0676021{col 26}{space 2} .0183921{col 37}{space 1}    3.68{col 46}{space 3}0.000{col 54}{space 4} .0313473{col 67}{space 3} .1038569
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0149809{col 26}{space 2} .0080836{col 37}{space 1}    1.85{col 46}{space 3}0.065{col 54}{space 4}-.0009536{col 67}{space 3} .0309155
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0724605{col 26}{space 2} .0202078{col 37}{space 1}    3.59{col 46}{space 3}0.000{col 54}{space 4} .0326265{col 67}{space 3} .1122945
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} -.093513{col 26}{space 2} .0519003{col 37}{space 1}   -1.80{col 46}{space 3}0.073{col 54}{space 4}-.1958198{col 67}{space 3} .0087938
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_women.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_women.tex"'})

{com}. 
. file open stats using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\describe\stats.txt", write replace
{txt}
{com}. 
. 
. ///Are the effects in the fourth column signf. different from each other?
> set matsize 11000
{txt}
{com}. 
. ///fit the two models
> eststo clear
{txt}
{com}. quietly reg voted_k stdcoshare $cov ib(last).cohortbycountry i.Forsamling if voted_coethnics_cum>0.75 & sex==1
{txt}
{com}. estimates store man
{txt}
{com}. 
. quietly reg voted_k stdcoshare $cov ib(last).cohortbycountry i.Forsamling if voted_coethnics_cum>0.75 & sex==0
{txt}
{com}. estimates store wom
{txt}
{com}. 
. *Common covariance-matrix 
. suest man wom, vce(cluster Kommun)
{res}
{txt}Simultaneous results for {stata estimates replay man:man}, {stata estimates replay wom:wom}

{col 49}Number of obs{col 67}= {res}    15,301

{txt}{ralign 78:(Std. Err. adjusted for {res:254} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}man_mean     {txt}{c |}
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{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 5}230901  {c |}{col 14}{res}{space 2}-.4301999{col 26}{space 2} .0321075{col 37}{space 1}  -13.40{col 46}{space 3}0.000{col 54}{space 4}-.4931294{col 67}{space 3}-.3672704
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{txt}{space 5}236109  {c |}{col 14}{res}{space 2} .3135389{col 26}{space 2} .0487651{col 37}{space 1}    6.43{col 46}{space 3}0.000{col 54}{space 4}  .217961{col 67}{space 3} .4091168
{txt}{space 5}238007  {c |}{col 14}{res}{space 2} .3709905{col 26}{space 2} .0273519{col 37}{space 1}   13.56{col 46}{space 3}0.000{col 54}{space 4} .3173818{col 67}{space 3} .4245992
{txt}{space 5}240102  {c |}{col 14}{res}{space 2}-.4561349{col 26}{space 2} .0312417{col 37}{space 1}  -14.60{col 46}{space 3}0.000{col 54}{space 4}-.5173676{col 67}{space 3}-.3949023
{txt}{space 5}240401  {c |}{col 14}{res}{space 2} .5344376{col 26}{space 2} .0147022{col 37}{space 1}   36.35{col 46}{space 3}0.000{col 54}{space 4} .5056218{col 67}{space 3} .5632534
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{txt}{space 5}241702  {c |}{col 14}{res}{space 2} .4859756{col 26}{space 2} .0870014{col 37}{space 1}    5.59{col 46}{space 3}0.000{col 54}{space 4} .3154559{col 67}{space 3} .6564953
{txt}{space 5}242101  {c |}{col 14}{res}{space 2}   .52256{col 26}{space 2} .0384546{col 37}{space 1}   13.59{col 46}{space 3}0.000{col 54}{space 4} .4471904{col 67}{space 3} .5979296
{txt}{space 5}246301  {c |}{col 14}{res}{space 2}-.5480206{col 26}{space 2} .1086129{col 37}{space 1}   -5.05{col 46}{space 3}0.000{col 54}{space 4} -.760898{col 67}{space 3}-.3351433
{txt}{space 5}248001  {c |}{col 14}{res}{space 2} .0127832{col 26}{space 2} .0226564{col 37}{space 1}    0.56{col 46}{space 3}0.573{col 54}{space 4}-.0316224{col 67}{space 3} .0571889
{txt}{space 5}248002  {c |}{col 14}{res}{space 2}-.0603095{col 26}{space 2} .0277577{col 37}{space 1}   -2.17{col 46}{space 3}0.030{col 54}{space 4}-.1147136{col 67}{space 3}-.0059054
{txt}{space 5}248004  {c |}{col 14}{res}{space 2} .1263353{col 26}{space 2} .0586287{col 37}{space 1}    2.15{col 46}{space 3}0.031{col 54}{space 4} .0114252{col 67}{space 3} .2412453
{txt}{space 5}248005  {c |}{col 14}{res}{space 2}-.0072755{col 26}{space 2} .0209648{col 37}{space 1}   -0.35{col 46}{space 3}0.729{col 54}{space 4}-.0483657{col 67}{space 3} .0338148
{txt}{space 5}248006  {c |}{col 14}{res}{space 2} .0760736{col 26}{space 2} .0737705{col 37}{space 1}    1.03{col 46}{space 3}0.302{col 54}{space 4}-.0685139{col 67}{space 3} .2206611
{txt}{space 5}248101  {c |}{col 14}{res}{space 2}  .504944{col 26}{space 2} .0392169{col 37}{space 1}   12.88{col 46}{space 3}0.000{col 54}{space 4} .4280802{col 67}{space 3} .5818077
{txt}{space 5}248103  {c |}{col 14}{res}{space 2} .1152214{col 26}{space 2}  .051808{col 37}{space 1}    2.22{col 46}{space 3}0.026{col 54}{space 4} .0136796{col 67}{space 3} .2167632
{txt}{space 5}248201  {c |}{col 14}{res}{space 2}-.1630144{col 26}{space 2} .0337166{col 37}{space 1}   -4.83{col 46}{space 3}0.000{col 54}{space 4}-.2290978{col 67}{space 3} -.096931
{txt}{space 5}248202  {c |}{col 14}{res}{space 2}-.5786255{col 26}{space 2} .0292591{col 37}{space 1}  -19.78{col 46}{space 3}0.000{col 54}{space 4}-.6359723{col 67}{space 3}-.5212787
{txt}{space 5}248205  {c |}{col 14}{res}{space 2}-.5952315{col 26}{space 2} .0451034{col 37}{space 1}  -13.20{col 46}{space 3}0.000{col 54}{space 4}-.6836324{col 67}{space 3}-.5068305
{txt}{space 5}251801  {c |}{col 14}{res}{space 2} .2253345{col 26}{space 2}  .071508{col 37}{space 1}    3.15{col 46}{space 3}0.002{col 54}{space 4} .0851815{col 67}{space 3} .3654876
{txt}{space 5}252101  {c |}{col 14}{res}{space 2}  .190132{col 26}{space 2} .0478014{col 37}{space 1}    3.98{col 46}{space 3}0.000{col 54}{space 4} .0964429{col 67}{space 3} .2838211
{txt}{space 5}252301  {c |}{col 14}{res}{space 2} .1520681{col 26}{space 2} .0449999{col 37}{space 1}    3.38{col 46}{space 3}0.001{col 54}{space 4}   .06387{col 67}{space 3} .2402662
{txt}{space 5}256002  {c |}{col 14}{res}{space 2} .4769416{col 26}{space 2} .0357739{col 37}{space 1}   13.33{col 46}{space 3}0.000{col 54}{space 4}  .406826{col 67}{space 3} .5470571
{txt}{space 5}258001  {c |}{col 14}{res}{space 2} .0877901{col 26}{space 2} .0274079{col 37}{space 1}    3.20{col 46}{space 3}0.001{col 54}{space 4} .0340716{col 67}{space 3} .1415086
{txt}{space 5}258002  {c |}{col 14}{res}{space 2}-.2219214{col 26}{space 2} .0541183{col 37}{space 1}   -4.10{col 46}{space 3}0.000{col 54}{space 4}-.3279913{col 67}{space 3}-.1158514
{txt}{space 5}258003  {c |}{col 14}{res}{space 2} .1156368{col 26}{space 2} .0283513{col 37}{space 1}    4.08{col 46}{space 3}0.000{col 54}{space 4} .0600692{col 67}{space 3} .1712043
{txt}{space 5}258004  {c |}{col 14}{res}{space 2}-.4292629{col 26}{space 2} .0228038{col 37}{space 1}  -18.82{col 46}{space 3}0.000{col 54}{space 4}-.4739574{col 67}{space 3}-.3845683
{txt}{space 5}258103  {c |}{col 14}{res}{space 2} .4757025{col 26}{space 2} .0443413{col 37}{space 1}   10.73{col 46}{space 3}0.000{col 54}{space 4} .3887952{col 67}{space 3} .5626099
{txt}{space 5}258201  {c |}{col 14}{res}{space 2} .0169971{col 26}{space 2} .0254751{col 37}{space 1}    0.67{col 46}{space 3}0.505{col 54}{space 4}-.0329332{col 67}{space 3} .0669275
{txt}{space 5}258401  {c |}{col 14}{res}{space 2}-.3999678{col 26}{space 2} .1048315{col 37}{space 1}   -3.82{col 46}{space 3}0.000{col 54}{space 4}-.6054336{col 67}{space 3}-.1945019
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .2070369{col 26}{space 2} .0721082{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .0657074{col 67}{space 3} .3483664
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}man_lnvar    {txt}{c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.484051{col 26}{space 2} .0113924{col 37}{space 1} -130.27{col 46}{space 3}0.000{col 54}{space 4} -1.50638{col 67}{space 3}-1.461722
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}wom_mean     {txt}{c |}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0156129{col 26}{space 2} .0118465{col 37}{space 1}    1.32{col 46}{space 3}0.188{col 54}{space 4}-.0076059{col 67}{space 3} .0388316
{txt}{space 9}sex {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
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{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001767{col 26}{space 2} .0000253{col 37}{space 1}   -6.97{col 46}{space 3}0.000{col 54}{space 4}-.0002263{col 67}{space 3} -.000127
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0202254{col 26}{space 2} .0129089{col 37}{space 1}    1.57{col 46}{space 3}0.117{col 54}{space 4}-.0050757{col 67}{space 3} .0455264
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0676021{col 26}{space 2} .0175166{col 37}{space 1}    3.86{col 46}{space 3}0.000{col 54}{space 4} .0332703{col 67}{space 3} .1019339
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0149809{col 26}{space 2} .0076988{col 37}{space 1}    1.95{col 46}{space 3}0.052{col 54}{space 4}-.0001085{col 67}{space 3} .0300703
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0724605{col 26}{space 2} .0192458{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} .0347393{col 67}{space 3} .1101817
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0568964{col 26}{space 2} .2237577{col 37}{space 1}   -0.25{col 46}{space 3}0.799{col 54}{space 4}-.4954534{col 67}{space 3} .3816606
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3424736{col 26}{space 2} .1916968{col 37}{space 1}    1.79{col 46}{space 3}0.074{col 54}{space 4}-.0332451{col 67}{space 3} .7181923
{txt}{space 10}5  {c |}{col 14}{res}{space 2}  .136611{col 26}{space 2} .2188186{col 37}{space 1}    0.62{col 46}{space 3}0.532{col 54}{space 4}-.2922656{col 67}{space 3} .5654877
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0561324{col 26}{space 2} .0591906{col 37}{space 1}   -0.95{col 46}{space 3}0.343{col 54}{space 4}-.1721439{col 67}{space 3}  .059879
{txt}{space 10}7  {c |}{col 14}{res}{space 2}-.0100893{col 26}{space 2}  .058206{col 37}{space 1}   -0.17{col 46}{space 3}0.862{col 54}{space 4}-.1241709{col 67}{space 3} .1039923
{txt}{space 10}8  {c |}{col 14}{res}{space 2}-.0773429{col 26}{space 2} .0434293{col 37}{space 1}   -1.78{col 46}{space 3}0.075{col 54}{space 4}-.1624628{col 67}{space 3}  .007777
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.1632256{col 26}{space 2} .0408227{col 37}{space 1}   -4.00{col 46}{space 3}0.000{col 54}{space 4}-.2432366{col 67}{space 3}-.0832146
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.1282509{col 26}{space 2} .0512361{col 37}{space 1}   -2.50{col 46}{space 3}0.012{col 54}{space 4}-.2286718{col 67}{space 3}-.0278299
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.0353723{col 26}{space 2} .1270934{col 37}{space 1}   -0.28{col 46}{space 3}0.781{col 54}{space 4}-.2844707{col 67}{space 3} .2137261
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .0548231{col 26}{space 2}   .11189{col 37}{space 1}    0.49{col 46}{space 3}0.624{col 54}{space 4}-.1644772{col 67}{space 3} .2741234
{txt}{space 9}13  {c |}{col 14}{res}{space 2}   .31518{col 26}{space 2} .1093411{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4} .1008754{col 67}{space 3} .5294846
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .1263237{col 26}{space 2} .0740348{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4}-.0187818{col 67}{space 3} .2714291
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .0568581{col 26}{space 2} .0740143{col 37}{space 1}    0.77{col 46}{space 3}0.442{col 54}{space 4}-.0882071{col 67}{space 3} .2019234
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .2618387{col 26}{space 2} .0452124{col 37}{space 1}    5.79{col 46}{space 3}0.000{col 54}{space 4} .1732241{col 67}{space 3} .3504534
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .2505108{col 26}{space 2} .0399559{col 37}{space 1}    6.27{col 46}{space 3}0.000{col 54}{space 4} .1721987{col 67}{space 3} .3288229
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .1930856{col 26}{space 2} .0448267{col 37}{space 1}    4.31{col 46}{space 3}0.000{col 54}{space 4} .1052268{col 67}{space 3} .2809444
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .1460269{col 26}{space 2} .0864267{col 37}{space 1}    1.69{col 46}{space 3}0.091{col 54}{space 4}-.0233664{col 67}{space 3} .3154202
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .1891588{col 26}{space 2} .0940854{col 37}{space 1}    2.01{col 46}{space 3}0.044{col 54}{space 4} .0047549{col 67}{space 3} .3735627
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .1969789{col 26}{space 2}  .057846{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .0836028{col 67}{space 3}  .310355
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .2417368{col 26}{space 2} .0663624{col 37}{space 1}    3.64{col 46}{space 3}0.000{col 54}{space 4} .1116689{col 67}{space 3} .3718046
{txt}{space 9}23  {c |}{col 14}{res}{space 2}  .232896{col 26}{space 2} .0448511{col 37}{space 1}    5.19{col 46}{space 3}0.000{col 54}{space 4} .1449894{col 67}{space 3} .3208026
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .1380626{col 26}{space 2} .0566361{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4} .0270579{col 67}{space 3} .2490673
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .1276895{col 26}{space 2} .0526425{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4} .0245121{col 67}{space 3} .2308669
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .1565887{col 26}{space 2} .0698909{col 37}{space 1}    2.24{col 46}{space 3}0.025{col 54}{space 4}  .019605{col 67}{space 3} .2935723
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .1141118{col 26}{space 2} .0696926{col 37}{space 1}    1.64{col 46}{space 3}0.102{col 54}{space 4}-.0224832{col 67}{space 3} .2507069
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .1140741{col 26}{space 2} .1520823{col 37}{space 1}    0.75{col 46}{space 3}0.453{col 54}{space 4}-.1840017{col 67}{space 3}   .41215
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .0806144{col 26}{space 2}  .114465{col 37}{space 1}    0.70{col 46}{space 3}0.481{col 54}{space 4}-.1437329{col 67}{space 3} .3049618
{txt}{space 9}30  {c |}{col 14}{res}{space 2} -.040966{col 26}{space 2}  .142934{col 37}{space 1}   -0.29{col 46}{space 3}0.774{col 54}{space 4}-.3211116{col 67}{space 3} .2391796
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .1038133{col 26}{space 2}  .053937{col 37}{space 1}    1.92{col 46}{space 3}0.054{col 54}{space 4}-.0019013{col 67}{space 3} .2095279
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .1498021{col 26}{space 2} .0384476{col 37}{space 1}    3.90{col 46}{space 3}0.000{col 54}{space 4} .0744461{col 67}{space 3} .2251581
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .0621693{col 26}{space 2} .0529136{col 37}{space 1}    1.17{col 46}{space 3}0.240{col 54}{space 4}-.0415394{col 67}{space 3} .1658781
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .0269875{col 26}{space 2} .0475123{col 37}{space 1}    0.57{col 46}{space 3}0.570{col 54}{space 4}-.0661349{col 67}{space 3} .1201099
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .0187218{col 26}{space 2} .0558269{col 37}{space 1}    0.34{col 46}{space 3}0.737{col 54}{space 4}-.0906969{col 67}{space 3} .1281404
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .1247258{col 26}{space 2} .0628685{col 37}{space 1}    1.98{col 46}{space 3}0.047{col 54}{space 4} .0015058{col 67}{space 3} .2479457
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{txt}{space 5}258003  {c |}{col 14}{res}{space 2} .2502978{col 26}{space 2} .0355433{col 37}{space 1}    7.04{col 46}{space 3}0.000{col 54}{space 4} .1806341{col 67}{space 3} .3199614
{txt}{space 5}258103  {c |}{col 14}{res}{space 2}-.1529598{col 26}{space 2}  .039743{col 37}{space 1}   -3.85{col 46}{space 3}0.000{col 54}{space 4}-.2308547{col 67}{space 3}-.0750649
{txt}{space 5}258201  {c |}{col 14}{res}{space 2} .1927849{col 26}{space 2} .0252274{col 37}{space 1}    7.64{col 46}{space 3}0.000{col 54}{space 4} .1433401{col 67}{space 3} .2422297
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .2604461{col 26}{space 2} .0759616{col 37}{space 1}    3.43{col 46}{space 3}0.001{col 54}{space 4} .1115642{col 67}{space 3} .4093281
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}wom_lnvar    {txt}{c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.498858{col 26}{space 2} .0155242{col 37}{space 1}  -96.55{col 46}{space 3}0.000{col 54}{space 4}-1.529285{col 67}{space 3}-1.468431
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *test
. test [man_mean]stdcoshare-[wom_mean]stdcoshare = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[man_mean]stdcoshare - [wom_mean]stdcoshare = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    0.88
{txt}{col 10}Prob > chi2 =  {res}  0.3473
{txt}
{com}. 
. *save to text
. file write stats "P-value Man vs Wom: `r(p)' "  
{txt}
{com}. 
. ///////////////////////////////////
> ///Short run effect split sample voting for nd in 1991 - split sample
> 
. sort votes_nd order
{txt}
{com}. gen CD3 = _n
{txt}
{com}. gen voted_nd_cum = CD3/N
{txt}
{com}. 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_nd_cum>0.5, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    30,490
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       596
{txt}{col 49}{help j_robustsingular##|_new:F(  76,    151)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1186
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0987
{txt}{col 49}Root MSE{col 67}= {res}    0.4744

{txt}{ralign 78:(Std. Err. adjusted for {res:152} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0017984{col 26}{space 2} .0071803{col 37}{space 1}   -0.25{col 46}{space 3}0.803{col 54}{space 4}-.0159852{col 67}{space 3} .0123884
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0175551{col 26}{space 2} .0061257{col 37}{space 1}    2.87{col 46}{space 3}0.005{col 54}{space 4} .0054519{col 67}{space 3} .0296584
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0199837{col 26}{space 2} .0013024{col 37}{space 1}   15.34{col 46}{space 3}0.000{col 54}{space 4} .0174104{col 67}{space 3}  .022557
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002349{col 26}{space 2} .0000146{col 37}{space 1}  -16.04{col 46}{space 3}0.000{col 54}{space 4}-.0002638{col 67}{space 3}-.0002059
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0078677{col 26}{space 2} .0070469{col 37}{space 1}    1.12{col 46}{space 3}0.266{col 54}{space 4}-.0060555{col 67}{space 3}  .021791
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0520313{col 26}{space 2} .0117068{col 37}{space 1}    4.44{col 46}{space 3}0.000{col 54}{space 4} .0289011{col 67}{space 3} .0751615
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0158536{col 26}{space 2} .0050777{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .0058211{col 67}{space 3}  .025886
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0793123{col 26}{space 2} .0069495{col 37}{space 1}   11.41{col 46}{space 3}0.000{col 54}{space 4} .0655816{col 67}{space 3}  .093043
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2}-.0419078{col 26}{space 2}  .125757{col 37}{space 1}   -0.33{col 46}{space 3}0.739{col 54}{space 4}-.2903784{col 67}{space 3} .2065629
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .071336{col 26}{space 2} .1473063{col 37}{space 1}    0.48{col 46}{space 3}0.629{col 54}{space 4}-.2197117{col 67}{space 3} .3623837
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2288592{col 26}{space 2} .2775828{col 37}{space 1}    0.82{col 46}{space 3}0.411{col 54}{space 4}-.3195885{col 67}{space 3}  .777307
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0950464{col 26}{space 2} .1393563{col 37}{space 1}   -0.68{col 46}{space 3}0.496{col 54}{space 4}-.3703865{col 67}{space 3} .1802936
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1716701{col 26}{space 2} .1199832{col 37}{space 1}    1.43{col 46}{space 3}0.155{col 54}{space 4}-.0653925{col 67}{space 3} .4087328
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2323529{col 26}{space 2} .1320055{col 37}{space 1}    1.76{col 46}{space 3}0.080{col 54}{space 4}-.0284635{col 67}{space 3} .4931693
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1802244{col 26}{space 2} .1327006{col 37}{space 1}    1.36{col 46}{space 3}0.176{col 54}{space 4}-.0819654{col 67}{space 3} .4424142
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1683634{col 26}{space 2} .1315663{col 37}{space 1}    1.28{col 46}{space 3}0.203{col 54}{space 4}-.0915852{col 67}{space 3} .4283119
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1992931{col 26}{space 2} .1494123{col 37}{space 1}    1.33{col 46}{space 3}0.184{col 54}{space 4}-.0959157{col 67}{space 3} .4945019
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .5072356{col 26}{space 2} .1430499{col 37}{space 1}    3.55{col 46}{space 3}0.001{col 54}{space 4} .2245978{col 67}{space 3} .7898734
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .4295435{col 26}{space 2} .1408678{col 37}{space 1}    3.05{col 46}{space 3}0.003{col 54}{space 4}  .151217{col 67}{space 3}   .70787
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3644907{col 26}{space 2} .1485806{col 37}{space 1}    2.45{col 46}{space 3}0.015{col 54}{space 4} .0709254{col 67}{space 3}  .658056
{txt}{space 9}14  {c |}{col 14}{res}{space 2}  .444807{col 26}{space 2} .1412284{col 37}{space 1}    3.15{col 46}{space 3}0.002{col 54}{space 4} .1657681{col 67}{space 3} .7238458
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .4729376{col 26}{space 2} .1607408{col 37}{space 1}    2.94{col 46}{space 3}0.004{col 54}{space 4} .1553462{col 67}{space 3} .7905291
{txt}{space 9}16  {c |}{col 14}{res}{space 2}   .55732{col 26}{space 2}  .125823{col 37}{space 1}    4.43{col 46}{space 3}0.000{col 54}{space 4} .3087191{col 67}{space 3} .8059209
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .5398909{col 26}{space 2} .1334305{col 37}{space 1}    4.05{col 46}{space 3}0.000{col 54}{space 4}  .276259{col 67}{space 3} .8035227
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .5248221{col 26}{space 2} .1252671{col 37}{space 1}    4.19{col 46}{space 3}0.000{col 54}{space 4} .2773195{col 67}{space 3} .7723246
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .4725863{col 26}{space 2} .1257788{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} .2240727{col 67}{space 3} .7210999
{txt}{space 9}20  {c |}{col 14}{res}{space 2}  .500801{col 26}{space 2} .1403635{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .2234709{col 67}{space 3}  .778131
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .4818118{col 26}{space 2} .1303638{col 37}{space 1}    3.70{col 46}{space 3}0.000{col 54}{space 4} .2242392{col 67}{space 3} .7393845
{txt}{space 9}22  {c |}{col 14}{res}{space 2}  .479499{col 26}{space 2} .1285479{col 37}{space 1}    3.73{col 46}{space 3}0.000{col 54}{space 4} .2255142{col 67}{space 3} .7334838
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .4963924{col 26}{space 2}  .132313{col 37}{space 1}    3.75{col 46}{space 3}0.000{col 54}{space 4} .2349686{col 67}{space 3} .7578162
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .4420409{col 26}{space 2} .1275407{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4}  .190046{col 67}{space 3} .6940357
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .4184331{col 26}{space 2} .1469631{col 37}{space 1}    2.85{col 46}{space 3}0.005{col 54}{space 4} .1280635{col 67}{space 3} .7088027
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .5188856{col 26}{space 2} .1499992{col 37}{space 1}    3.46{col 46}{space 3}0.001{col 54}{space 4} .2225173{col 67}{space 3} .8152539
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .4614932{col 26}{space 2} .1433652{col 37}{space 1}    3.22{col 46}{space 3}0.002{col 54}{space 4} .1782325{col 67}{space 3}  .744754
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .3363434{col 26}{space 2} .1319019{col 37}{space 1}    2.55{col 46}{space 3}0.012{col 54}{space 4} .0757317{col 67}{space 3}  .596955
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .3123131{col 26}{space 2} .1398713{col 37}{space 1}    2.23{col 46}{space 3}0.027{col 54}{space 4} .0359554{col 67}{space 3} .5886707
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .2179387{col 26}{space 2}  .144672{col 37}{space 1}    1.51{col 46}{space 3}0.134{col 54}{space 4}-.0679041{col 67}{space 3} .5037814
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .4577838{col 26}{space 2} .1401987{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .1807793{col 67}{space 3} .7347883
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .4513064{col 26}{space 2} .1387868{col 37}{space 1}    3.25{col 46}{space 3}0.001{col 54}{space 4} .1770915{col 67}{space 3} .7255212
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .4002895{col 26}{space 2} .1366135{col 37}{space 1}    2.93{col 46}{space 3}0.004{col 54}{space 4} .1303687{col 67}{space 3} .6702104
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .3081785{col 26}{space 2} .1302262{col 37}{space 1}    2.37{col 46}{space 3}0.019{col 54}{space 4} .0508776{col 67}{space 3} .5654793
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .3263618{col 26}{space 2} .1354269{col 37}{space 1}    2.41{col 46}{space 3}0.017{col 54}{space 4} .0587854{col 67}{space 3} .5939382
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .4500979{col 26}{space 2} .1756896{col 37}{space 1}    2.56{col 46}{space 3}0.011{col 54}{space 4} .1029705{col 67}{space 3} .7972252
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .4038289{col 26}{space 2}  .146366{col 37}{space 1}    2.76{col 46}{space 3}0.007{col 54}{space 4} .1146392{col 67}{space 3} .6930186
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .4674223{col 26}{space 2} .1348858{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4} .2009151{col 67}{space 3} .7339294
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .3961534{col 26}{space 2} .1504095{col 37}{space 1}    2.63{col 46}{space 3}0.009{col 54}{space 4} .0989744{col 67}{space 3} .6933324
{txt}{space 9}40  {c |}{col 14}{res}{space 2}   .30978{col 26}{space 2}  .121954{col 37}{space 1}    2.54{col 46}{space 3}0.012{col 54}{space 4} .0688234{col 67}{space 3} .5507366
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .4295124{col 26}{space 2}  .124338{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4} .1838456{col 67}{space 3} .6751793
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .4430782{col 26}{space 2} .1294012{col 37}{space 1}    3.42{col 46}{space 3}0.001{col 54}{space 4} .1874074{col 67}{space 3}  .698749
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .3871624{col 26}{space 2} .1302819{col 37}{space 1}    2.97{col 46}{space 3}0.003{col 54}{space 4} .1297515{col 67}{space 3} .6445733
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .3328141{col 26}{space 2} .1278016{col 37}{space 1}    2.60{col 46}{space 3}0.010{col 54}{space 4} .0803038{col 67}{space 3} .5853245
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .3297961{col 26}{space 2} .1168328{col 37}{space 1}    2.82{col 46}{space 3}0.005{col 54}{space 4} .0989581{col 67}{space 3} .5606341
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .4176313{col 26}{space 2} .1237561{col 37}{space 1}    3.37{col 46}{space 3}0.001{col 54}{space 4} .1731142{col 67}{space 3} .6621484
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .4570022{col 26}{space 2} .1162946{col 37}{space 1}    3.93{col 46}{space 3}0.000{col 54}{space 4} .2272274{col 67}{space 3}  .686777
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .4832517{col 26}{space 2} .1331713{col 37}{space 1}    3.63{col 46}{space 3}0.000{col 54}{space 4}  .220132{col 67}{space 3} .7463714
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .3665428{col 26}{space 2} .1176659{col 37}{space 1}    3.12{col 46}{space 3}0.002{col 54}{space 4} .1340586{col 67}{space 3} .5990271
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .3600465{col 26}{space 2} .1199465{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .1230563{col 67}{space 3} .5970368
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .5703456{col 26}{space 2} .1376905{col 37}{space 1}    4.14{col 46}{space 3}0.000{col 54}{space 4} .2982969{col 67}{space 3} .8423943
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .4668777{col 26}{space 2} .1367439{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .1966992{col 67}{space 3} .7370561
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .4392162{col 26}{space 2} .1457287{col 37}{space 1}    3.01{col 46}{space 3}0.003{col 54}{space 4} .1512857{col 67}{space 3} .7271468
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .3850838{col 26}{space 2} .1471646{col 37}{space 1}    2.62{col 46}{space 3}0.010{col 54}{space 4}  .094316{col 67}{space 3} .6758515
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .4028842{col 26}{space 2} .1588812{col 37}{space 1}    2.54{col 46}{space 3}0.012{col 54}{space 4}  .088967{col 67}{space 3} .7168014
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .2119325{col 26}{space 2} .1328208{col 37}{space 1}    1.60{col 46}{space 3}0.113{col 54}{space 4}-.0504947{col 67}{space 3} .4743598
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .1448963{col 26}{space 2} .1225016{col 37}{space 1}    1.18{col 46}{space 3}0.239{col 54}{space 4}-.0971422{col 67}{space 3} .3869349
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .1622986{col 26}{space 2} .1188425{col 37}{space 1}    1.37{col 46}{space 3}0.174{col 54}{space 4}-.0725103{col 67}{space 3} .3971075
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .0890269{col 26}{space 2}  .109094{col 37}{space 1}    0.82{col 46}{space 3}0.416{col 54}{space 4}-.1265209{col 67}{space 3} .3045747
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .1385097{col 26}{space 2} .1106476{col 37}{space 1}    1.25{col 46}{space 3}0.213{col 54}{space 4}-.0801078{col 67}{space 3} .3571271
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .4006229{col 26}{space 2} .1583039{col 37}{space 1}    2.53{col 46}{space 3}0.012{col 54}{space 4} .0878463{col 67}{space 3} .7133995
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .2890272{col 26}{space 2} .1314877{col 37}{space 1}    2.20{col 46}{space 3}0.029{col 54}{space 4}  .029234{col 67}{space 3} .5488205
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .3235643{col 26}{space 2} .1118455{col 37}{space 1}    2.89{col 46}{space 3}0.004{col 54}{space 4}   .10258{col 67}{space 3} .5445487
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .2567452{col 26}{space 2} .1322441{col 37}{space 1}    1.94{col 46}{space 3}0.054{col 54}{space 4}-.0045425{col 67}{space 3} .5180329
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .1293058{col 26}{space 2} .1199365{col 37}{space 1}    1.08{col 46}{space 3}0.283{col 54}{space 4}-.1076645{col 67}{space 3} .3662761
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .5063959{col 26}{space 2} .1574653{col 37}{space 1}    3.22{col 46}{space 3}0.002{col 54}{space 4} .1952761{col 67}{space 3} .8175156
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .4664632{col 26}{space 2} .1575026{col 37}{space 1}    2.96{col 46}{space 3}0.004{col 54}{space 4} .1552697{col 67}{space 3} .7776566
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .4927371{col 26}{space 2} .1700156{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4} .1568203{col 67}{space 3} .8286538
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .3834656{col 26}{space 2} .1480574{col 37}{space 1}    2.59{col 46}{space 3}0.011{col 54}{space 4} .0909341{col 67}{space 3} .6759972
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .3567933{col 26}{space 2} .1657186{col 37}{space 1}    2.15{col 46}{space 3}0.033{col 54}{space 4} .0293666{col 67}{space 3}   .68422
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3028658{col 26}{space 2} .1343629{col 37}{space 1}   -2.25{col 46}{space 3}0.026{col 54}{space 4}-.5683398{col 67}{space 3}-.0373918
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum<0.25 & voted_nd_cum>0.5, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 6.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     6,361
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       425
{txt}{col 49}{help j_robustsingular##|_new:F(  70,    144)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1829
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1136
{txt}{col 49}Root MSE{col 67}= {res}    0.4674

{txt}{ralign 78:(Std. Err. adjusted for {res:145} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} -.004511{col 26}{space 2} .0102471{col 37}{space 1}   -0.44{col 46}{space 3}0.660{col 54}{space 4}-.0247651{col 67}{space 3} .0157432
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0435743{col 26}{space 2} .0130314{col 37}{space 1}    3.34{col 46}{space 3}0.001{col 54}{space 4} .0178168{col 67}{space 3} .0693318
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0253086{col 26}{space 2} .0026717{col 37}{space 1}    9.47{col 46}{space 3}0.000{col 54}{space 4} .0200277{col 67}{space 3} .0305894
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0003078{col 26}{space 2} .0000306{col 37}{space 1}  -10.05{col 46}{space 3}0.000{col 54}{space 4}-.0003684{col 67}{space 3}-.0002473
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0108517{col 26}{space 2} .0215729{col 37}{space 1}    0.50{col 46}{space 3}0.616{col 54}{space 4}-.0317888{col 67}{space 3} .0534921
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0644078{col 26}{space 2} .0270191{col 37}{space 1}    2.38{col 46}{space 3}0.018{col 54}{space 4} .0110026{col 67}{space 3} .1178131
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0079584{col 26}{space 2} .0081043{col 37}{space 1}    0.98{col 46}{space 3}0.328{col 54}{space 4}-.0080603{col 67}{space 3} .0239771
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0675314{col 26}{space 2} .0173572{col 37}{space 1}    3.89{col 46}{space 3}0.000{col 54}{space 4} .0332236{col 67}{space 3} .1018392
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2} .1561606{col 26}{space 2} .0361446{col 37}{space 1}    4.32{col 46}{space 3}0.000{col 54}{space 4} .0847181{col 67}{space 3} .2276032
{txt}{space 10}6  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}7  {c |}{col 14}{res}{space 2}  .411378{col 26}{space 2} .0459373{col 37}{space 1}    8.96{col 46}{space 3}0.000{col 54}{space 4} .3205794{col 67}{space 3} .5021765
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .4625771{col 26}{space 2} .0813328{col 37}{space 1}    5.69{col 46}{space 3}0.000{col 54}{space 4} .3018168{col 67}{space 3} .6233375
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .4170051{col 26}{space 2} .1844735{col 37}{space 1}    2.26{col 46}{space 3}0.025{col 54}{space 4} .0523794{col 67}{space 3} .7816309
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .3400249{col 26}{space 2} .1226863{col 37}{space 1}    2.77{col 46}{space 3}0.006{col 54}{space 4} .0975262{col 67}{space 3} .5825236
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .9744466{col 26}{space 2} .0973558{col 37}{space 1}   10.01{col 46}{space 3}0.000{col 54}{space 4} .7820156{col 67}{space 3} 1.166878
{txt}{space 9}12  {c |}{col 14}{res}{space 2}  .736649{col 26}{space 2} .0995945{col 37}{space 1}    7.40{col 46}{space 3}0.000{col 54}{space 4}  .539793{col 67}{space 3} .9335049
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .7266649{col 26}{space 2} .1475904{col 37}{space 1}    4.92{col 46}{space 3}0.000{col 54}{space 4} .4349413{col 67}{space 3} 1.018388
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .7520018{col 26}{space 2} .1117928{col 37}{space 1}    6.73{col 46}{space 3}0.000{col 54}{space 4}  .531035{col 67}{space 3} .9729686
{txt}{space 9}15  {c |}{col 14}{res}{space 2}  .946705{col 26}{space 2} .1305261{col 37}{space 1}    7.25{col 46}{space 3}0.000{col 54}{space 4} .6887104{col 67}{space 3}   1.2047
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .9113235{col 26}{space 2}  .053546{col 37}{space 1}   17.02{col 46}{space 3}0.000{col 54}{space 4} .8054857{col 67}{space 3} 1.017161
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .8584482{col 26}{space 2}  .055989{col 37}{space 1}   15.33{col 46}{space 3}0.000{col 54}{space 4} .7477818{col 67}{space 3} .9691146
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .8358655{col 26}{space 2} .0385298{col 37}{space 1}   21.69{col 46}{space 3}0.000{col 54}{space 4} .7597085{col 67}{space 3} .9120225
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .8171808{col 26}{space 2} .0946837{col 37}{space 1}    8.63{col 46}{space 3}0.000{col 54}{space 4} .6300314{col 67}{space 3}  1.00433
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .6294439{col 26}{space 2} .1520593{col 37}{space 1}    4.14{col 46}{space 3}0.000{col 54}{space 4} .3288873{col 67}{space 3} .9300005
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .1250677{col 26}{space 2} .0524114{col 37}{space 1}    2.39{col 46}{space 3}0.018{col 54}{space 4} .0214727{col 67}{space 3} .2286627
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .8601787{col 26}{space 2} .3391423{col 37}{space 1}    2.54{col 46}{space 3}0.012{col 54}{space 4} .1898384{col 67}{space 3} 1.530519
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .9455812{col 26}{space 2} .0956487{col 37}{space 1}    9.89{col 46}{space 3}0.000{col 54}{space 4} .7565243{col 67}{space 3} 1.134638
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .5197466{col 26}{space 2} .1166306{col 37}{space 1}    4.46{col 46}{space 3}0.000{col 54}{space 4} .2892174{col 67}{space 3} .7502758
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .8157528{col 26}{space 2}  .170959{col 37}{space 1}    4.77{col 46}{space 3}0.000{col 54}{space 4} .4778395{col 67}{space 3} 1.153666
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .9467051{col 26}{space 2} .0674788{col 37}{space 1}   14.03{col 46}{space 3}0.000{col 54}{space 4} .8133282{col 67}{space 3} 1.080082
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .8420239{col 26}{space 2} .0419536{col 37}{space 1}   20.07{col 46}{space 3}0.000{col 54}{space 4} .7590994{col 67}{space 3} .9249483
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .6423398{col 26}{space 2} .0363487{col 37}{space 1}   17.67{col 46}{space 3}0.000{col 54}{space 4} .5704939{col 67}{space 3} .7141857
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .6822834{col 26}{space 2} .0488584{col 37}{space 1}   13.96{col 46}{space 3}0.000{col 54}{space 4} .5857112{col 67}{space 3} .7788556
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .5676874{col 26}{space 2}  .039382{col 37}{space 1}   14.41{col 46}{space 3}0.000{col 54}{space 4} .4898459{col 67}{space 3} .6455289
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .8958405{col 26}{space 2} .1041621{col 37}{space 1}    8.60{col 46}{space 3}0.000{col 54}{space 4} .6899563{col 67}{space 3} 1.101725
{txt}{space 9}32  {c |}{col 14}{res}{space 2}  .869918{col 26}{space 2} .0621254{col 37}{space 1}   14.00{col 46}{space 3}0.000{col 54}{space 4} .7471225{col 67}{space 3} .9927134
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .7643012{col 26}{space 2}   .04925{col 37}{space 1}   15.52{col 46}{space 3}0.000{col 54}{space 4} .6669548{col 67}{space 3} .8616476
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .6749502{col 26}{space 2} .0567879{col 37}{space 1}   11.89{col 46}{space 3}0.000{col 54}{space 4} .5627048{col 67}{space 3} .7871957
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .6016299{col 26}{space 2} .0570658{col 37}{space 1}   10.54{col 46}{space 3}0.000{col 54}{space 4}  .488835{col 67}{space 3} .7144248
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .6942196{col 26}{space 2} .2323931{col 37}{space 1}    2.99{col 46}{space 3}0.003{col 54}{space 4} .2348772{col 67}{space 3} 1.153562
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .8433404{col 26}{space 2} .1670559{col 37}{space 1}    5.05{col 46}{space 3}0.000{col 54}{space 4} .5131418{col 67}{space 3} 1.173539
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .8771043{col 26}{space 2}  .084303{col 37}{space 1}   10.40{col 46}{space 3}0.000{col 54}{space 4} .7104731{col 67}{space 3} 1.043736
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .6822954{col 26}{space 2} .1789363{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .3286143{col 67}{space 3} 1.035976
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .6938011{col 26}{space 2} .0858879{col 37}{space 1}    8.08{col 46}{space 3}0.000{col 54}{space 4} .5240373{col 67}{space 3}  .863565
{txt}{space 9}41  {c |}{col 14}{res}{space 2}  .823688{col 26}{space 2} .0357395{col 37}{space 1}   23.05{col 46}{space 3}0.000{col 54}{space 4} .7530461{col 67}{space 3} .8943299
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .8420068{col 26}{space 2} .0340678{col 37}{space 1}   24.72{col 46}{space 3}0.000{col 54}{space 4} .7746693{col 67}{space 3} .9093443
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .7611895{col 26}{space 2} .0422101{col 37}{space 1}   18.03{col 46}{space 3}0.000{col 54}{space 4} .6777582{col 67}{space 3} .8446209
{txt}{space 9}44  {c |}{col 14}{res}{space 2}  .684911{col 26}{space 2} .0482572{col 37}{space 1}   14.19{col 46}{space 3}0.000{col 54}{space 4} .5895269{col 67}{space 3} .7802951
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .7255511{col 26}{space 2} .0475188{col 37}{space 1}   15.27{col 46}{space 3}0.000{col 54}{space 4} .6316267{col 67}{space 3} .8194756
{txt}{space 9}46  {c |}{col 14}{res}{space 2}  .667997{col 26}{space 2} .0689202{col 37}{space 1}    9.69{col 46}{space 3}0.000{col 54}{space 4}  .531771{col 67}{space 3} .8042231
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .6982235{col 26}{space 2} .0431428{col 37}{space 1}   16.18{col 46}{space 3}0.000{col 54}{space 4} .6129486{col 67}{space 3} .7834984
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .8432603{col 26}{space 2} .0481627{col 37}{space 1}   17.51{col 46}{space 3}0.000{col 54}{space 4} .7480632{col 67}{space 3} .9384574
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .6873272{col 26}{space 2} .0433818{col 37}{space 1}   15.84{col 46}{space 3}0.000{col 54}{space 4} .6015798{col 67}{space 3} .7730747
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .6714903{col 26}{space 2} .0621245{col 37}{space 1}   10.81{col 46}{space 3}0.000{col 54}{space 4} .5486966{col 67}{space 3}  .794284
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .8984856{col 26}{space 2} .0986986{col 37}{space 1}    9.10{col 46}{space 3}0.000{col 54}{space 4} .7034004{col 67}{space 3} 1.093571
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .5832899{col 26}{space 2} .2028327{col 37}{space 1}    2.88{col 46}{space 3}0.005{col 54}{space 4} .1823759{col 67}{space 3} .9842039
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .9332852{col 26}{space 2} .1288673{col 37}{space 1}    7.24{col 46}{space 3}0.000{col 54}{space 4} .6785693{col 67}{space 3} 1.188001
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .7460353{col 26}{space 2} .1871386{col 37}{space 1}    3.99{col 46}{space 3}0.000{col 54}{space 4} .3761418{col 67}{space 3} 1.115929
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .2969803{col 26}{space 2} .0683143{col 37}{space 1}    4.35{col 46}{space 3}0.000{col 54}{space 4}  .161952{col 67}{space 3} .4320086
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .2526915{col 26}{space 2} .2194592{col 37}{space 1}    1.15{col 46}{space 3}0.251{col 54}{space 4}-.1810861{col 67}{space 3}  .686469
{txt}{space 9}57  {c |}{col 14}{res}{space 2}  .825808{col 26}{space 2} .2777982{col 37}{space 1}    2.97{col 46}{space 3}0.003{col 54}{space 4} .2767189{col 67}{space 3} 1.374897
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .5742738{col 26}{space 2} .1073822{col 37}{space 1}    5.35{col 46}{space 3}0.000{col 54}{space 4} .3620248{col 67}{space 3} .7865229
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .3994647{col 26}{space 2} .1739188{col 37}{space 1}    2.30{col 46}{space 3}0.023{col 54}{space 4}  .055701{col 67}{space 3} .7432283
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .3110649{col 26}{space 2} .0566674{col 37}{space 1}    5.49{col 46}{space 3}0.000{col 54}{space 4} .1990575{col 67}{space 3} .4230723
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .8717674{col 26}{space 2} .1062641{col 37}{space 1}    8.20{col 46}{space 3}0.000{col 54}{space 4} .6617285{col 67}{space 3} 1.081806
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .6973872{col 26}{space 2} .1777915{col 37}{space 1}    3.92{col 46}{space 3}0.000{col 54}{space 4} .3459689{col 67}{space 3} 1.048805
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .4576143{col 26}{space 2}  .087235{col 37}{space 1}    5.25{col 46}{space 3}0.000{col 54}{space 4} .2851877{col 67}{space 3} .6300409
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .5388257{col 26}{space 2} .0675769{col 37}{space 1}    7.97{col 46}{space 3}0.000{col 54}{space 4} .4052548{col 67}{space 3} .6723966
{txt}{space 9}65  {c |}{col 14}{res}{space 2}  .518729{col 26}{space 2} .0960089{col 37}{space 1}    5.40{col 46}{space 3}0.000{col 54}{space 4} .3289603{col 67}{space 3} .7084978
{txt}{space 9}66  {c |}{col 14}{res}{space 2} 1.008914{col 26}{space 2} .1611189{col 37}{space 1}    6.26{col 46}{space 3}0.000{col 54}{space 4} .6904499{col 67}{space 3} 1.327377
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .9471341{col 26}{space 2} .1202326{col 37}{space 1}    7.88{col 46}{space 3}0.000{col 54}{space 4} .7094854{col 67}{space 3} 1.184783
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .9608884{col 26}{space 2} .1513751{col 37}{space 1}    6.35{col 46}{space 3}0.000{col 54}{space 4} .6616841{col 67}{space 3} 1.260093
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .8532982{col 26}{space 2} .1299436{col 37}{space 1}    6.57{col 46}{space 3}0.000{col 54}{space 4} .5964549{col 67}{space 3} 1.110142
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .6778651{col 26}{space 2}  .105751{col 37}{space 1}    6.41{col 46}{space 3}0.000{col 54}{space 4} .4688402{col 67}{space 3} .8868899
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.7338251{col 26}{space 2} .0521528{col 37}{space 1}  -14.07{col 46}{space 3}0.000{col 54}{space 4}-.8369092{col 67}{space 3}-.6307411
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 & voted_nd_cum>0.5 , absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     7,584
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       395
{txt}{col 49}{help j_robustsingular##|_new:F(  70,    145)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1523
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0965
{txt}{col 49}Root MSE{col 67}= {res}    0.4745

{txt}{ralign 78:(Std. Err. adjusted for {res:146} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0278733{col 26}{space 2} .0184009{col 37}{space 1}   -1.51{col 46}{space 3}0.132{col 54}{space 4}-.0642419{col 67}{space 3} .0084954
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0283923{col 26}{space 2} .0173232{col 37}{space 1}    1.64{col 46}{space 3}0.103{col 54}{space 4}-.0058463{col 67}{space 3} .0626309
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0206018{col 26}{space 2}    .0043{col 37}{space 1}    4.79{col 46}{space 3}0.000{col 54}{space 4} .0121031{col 67}{space 3} .0291006
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002565{col 26}{space 2} .0000439{col 37}{space 1}   -5.84{col 46}{space 3}0.000{col 54}{space 4}-.0003434{col 67}{space 3}-.0001697
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0180783{col 26}{space 2} .0204195{col 37}{space 1}    0.89{col 46}{space 3}0.377{col 54}{space 4}-.0222799{col 67}{space 3} .0584366
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0412016{col 26}{space 2}  .024054{col 37}{space 1}    1.71{col 46}{space 3}0.089{col 54}{space 4}-.0063402{col 67}{space 3} .0887434
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0179153{col 26}{space 2} .0119966{col 37}{space 1}    1.49{col 46}{space 3}0.138{col 54}{space 4}-.0057956{col 67}{space 3} .0416261
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0785829{col 26}{space 2} .0158044{col 37}{space 1}    4.97{col 46}{space 3}0.000{col 54}{space 4} .0473462{col 67}{space 3} .1098197
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}4  {c |}{col 14}{res}{space 2}-.0426378{col 26}{space 2} .1176331{col 37}{space 1}   -0.36{col 46}{space 3}0.718{col 54}{space 4}-.2751349{col 67}{space 3} .1898593
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0900893{col 26}{space 2} .0971025{col 37}{space 1}   -0.93{col 46}{space 3}0.355{col 54}{space 4}-.2820084{col 67}{space 3} .1018299
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2964817{col 26}{space 2} .1225079{col 37}{space 1}    2.42{col 46}{space 3}0.017{col 54}{space 4} .0543499{col 67}{space 3} .5386136
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2} .4992206{col 26}{space 2}  .237311{col 37}{space 1}    2.10{col 46}{space 3}0.037{col 54}{space 4}  .030185{col 67}{space 3} .9682562
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .4347228{col 26}{space 2} .1501407{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4} .1379759{col 67}{space 3} .7314698
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .2522063{col 26}{space 2} .1746492{col 37}{space 1}    1.44{col 46}{space 3}0.151{col 54}{space 4}-.0929808{col 67}{space 3} .5973934
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .6734205{col 26}{space 2} .1858618{col 37}{space 1}    3.62{col 46}{space 3}0.000{col 54}{space 4} .3060721{col 67}{space 3} 1.040769
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .6098406{col 26}{space 2} .1483769{col 37}{space 1}    4.11{col 46}{space 3}0.000{col 54}{space 4} .3165798{col 67}{space 3} .9031015
{txt}{space 9}13  {c |}{col 14}{res}{space 2}  .450643{col 26}{space 2} .1797852{col 37}{space 1}    2.51{col 46}{space 3}0.013{col 54}{space 4} .0953047{col 67}{space 3} .8059812
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{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5627398{col 26}{space 2} .1832804{col 37}{space 1}    3.07{col 46}{space 3}0.003{col 54}{space 4} .2004935{col 67}{space 3} .9249861
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{txt}{space 9}69  {c |}{col 14}{res}{space 2}  .721189{col 26}{space 2} .2078505{col 37}{space 1}    3.47{col 46}{space 3}0.001{col 54}{space 4} .3103809{col 67}{space 3} 1.131997
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .4967293{col 26}{space 2} .1759091{col 37}{space 1}    2.82{col 46}{space 3}0.005{col 54}{space 4} .1490521{col 67}{space 3} .8444066
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.4391463{col 26}{space 2} .1357343{col 37}{space 1}   -3.24{col 46}{space 3}0.002{col 54}{space 4}-.7074197{col 67}{space 3}-.1708728
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 & voted_nd_cum>0.5 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     7,949
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       243
{txt}{col 49}{help j_robustsingular##|_new:F(  71,    103)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1458
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1107
{txt}{col 49}Root MSE{col 67}= {res}    0.4715

{txt}{ralign 78:(Std. Err. adjusted for {res:104} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0064183{col 26}{space 2} .0162268{col 37}{space 1}    0.40{col 46}{space 3}0.693{col 54}{space 4}-.0257638{col 67}{space 3} .0386003
{txt}{space 9}sex {c |}{col 14}{res}{space 2}-.0044822{col 26}{space 2} .0091798{col 37}{space 1}   -0.49{col 46}{space 3}0.626{col 54}{space 4}-.0226881{col 67}{space 3} .0137237
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0208659{col 26}{space 2} .0024014{col 37}{space 1}    8.69{col 46}{space 3}0.000{col 54}{space 4} .0161033{col 67}{space 3} .0256286
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002408{col 26}{space 2} .0000258{col 37}{space 1}   -9.33{col 46}{space 3}0.000{col 54}{space 4} -.000292{col 67}{space 3}-.0001896
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0043333{col 26}{space 2}   .01416{col 37}{space 1}    0.31{col 46}{space 3}0.760{col 54}{space 4}-.0237497{col 67}{space 3} .0324162
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0443629{col 26}{space 2} .0217858{col 37}{space 1}    2.04{col 46}{space 3}0.044{col 54}{space 4} .0011558{col 67}{space 3}   .08757
{txt}{space 4}children {c |}{col 14}{res}{space 2}  .012115{col 26}{space 2} .0107499{col 37}{space 1}    1.13{col 46}{space 3}0.262{col 54}{space 4} -.009205{col 67}{space 3} .0334349
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0877156{col 26}{space 2} .0156518{col 37}{space 1}    5.60{col 46}{space 3}0.000{col 54}{space 4} .0566739{col 67}{space 3} .1187574
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}7  {c |}{col 14}{res}{space 2} .1077255{col 26}{space 2}  .148618{col 37}{space 1}    0.72{col 46}{space 3}0.470{col 54}{space 4}-.1870232{col 67}{space 3} .4024742
{txt}{space 10}8  {c |}{col 14}{res}{space 2}  -.07872{col 26}{space 2} .1174152{col 37}{space 1}   -0.67{col 46}{space 3}0.504{col 54}{space 4}-.3115854{col 67}{space 3} .1541455
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.0004276{col 26}{space 2} .1038395{col 37}{space 1}   -0.00{col 46}{space 3}0.997{col 54}{space 4}-.2063688{col 67}{space 3} .2055136
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0337441{col 26}{space 2} .1566684{col 37}{space 1}    0.22{col 46}{space 3}0.830{col 54}{space 4}-.2769707{col 67}{space 3} .3444589
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4097082{col 26}{space 2} .1600692{col 37}{space 1}    2.56{col 46}{space 3}0.012{col 54}{space 4} .0922486{col 67}{space 3} .7271678
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .2371566{col 26}{space 2} .1316774{col 37}{space 1}    1.80{col 46}{space 3}0.075{col 54}{space 4}-.0239944{col 67}{space 3} .4983076
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .1841585{col 26}{space 2} .1504919{col 37}{space 1}    1.22{col 46}{space 3}0.224{col 54}{space 4}-.1143066{col 67}{space 3} .4826236
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .3057012{col 26}{space 2} .1176029{col 37}{space 1}    2.60{col 46}{space 3}0.011{col 54}{space 4} .0724636{col 67}{space 3} .5389389
{txt}{space 9}15  {c |}{col 14}{res}{space 2}  .226583{col 26}{space 2} .2095061{col 37}{space 1}    1.08{col 46}{space 3}0.282{col 54}{space 4}-.1889228{col 67}{space 3} .6420889
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .4221596{col 26}{space 2} .0472787{col 37}{space 1}    8.93{col 46}{space 3}0.000{col 54}{space 4} .3283935{col 67}{space 3} .5159257
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{txt}{space 9}18  {c |}{col 14}{res}{space 2}  .402473{col 26}{space 2} .0343501{col 37}{space 1}   11.72{col 46}{space 3}0.000{col 54}{space 4} .3343476{col 67}{space 3} .4705984
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .3598303{col 26}{space 2} .0587958{col 37}{space 1}    6.12{col 46}{space 3}0.000{col 54}{space 4} .2432226{col 67}{space 3} .4764379
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{txt}{space 9}21  {c |}{col 14}{res}{space 2} .2196552{col 26}{space 2} .0442867{col 37}{space 1}    4.96{col 46}{space 3}0.000{col 54}{space 4} .1318231{col 67}{space 3} .3074874
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{txt}{space 9}29  {c |}{col 14}{res}{space 2} .1387424{col 26}{space 2}  .031704{col 37}{space 1}    4.38{col 46}{space 3}0.000{col 54}{space 4} .0758649{col 67}{space 3} .2016198
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .0811085{col 26}{space 2}  .031678{col 37}{space 1}    2.56{col 46}{space 3}0.012{col 54}{space 4} .0182826{col 67}{space 3} .1439343
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .4694184{col 26}{space 2}  .045891{col 37}{space 1}   10.23{col 46}{space 3}0.000{col 54}{space 4} .3784044{col 67}{space 3} .5604323
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .3812227{col 26}{space 2} .0576128{col 37}{space 1}    6.62{col 46}{space 3}0.000{col 54}{space 4} .2669612{col 67}{space 3} .4954841
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .3115397{col 26}{space 2} .0434228{col 37}{space 1}    7.17{col 46}{space 3}0.000{col 54}{space 4} .2254208{col 67}{space 3} .3976586
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{txt}{space 9}57  {c |}{col 14}{res}{space 2}-.0199316{col 26}{space 2} .0615033{col 37}{space 1}   -0.32{col 46}{space 3}0.747{col 54}{space 4}-.1419089{col 67}{space 3} .1020456
{txt}{space 9}58  {c |}{col 14}{res}{space 2}-.0383718{col 26}{space 2}  .060584{col 37}{space 1}   -0.63{col 46}{space 3}0.528{col 54}{space 4} -.158526{col 67}{space 3} .0817823
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{txt}{space 9}65  {c |}{col 14}{res}{space 2}-.0351094{col 26}{space 2} .0500752{col 37}{space 1}   -0.70{col 46}{space 3}0.485{col 54}{space 4}-.1344217{col 67}{space 3} .0642028
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{txt}{space 9}67  {c |}{col 14}{res}{space 2} .2862962{col 26}{space 2} .1231352{col 37}{space 1}    2.33{col 46}{space 3}0.022{col 54}{space 4} .0420867{col 67}{space 3} .5305058
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .3596251{col 26}{space 2} .0631848{col 37}{space 1}    5.69{col 46}{space 3}0.000{col 54}{space 4} .2343129{col 67}{space 3} .4849373
{txt}{space 9}69  {c |}{col 14}{res}{space 2}  .281162{col 26}{space 2} .0669291{col 37}{space 1}    4.20{col 46}{space 3}0.000{col 54}{space 4} .1484239{col 67}{space 3} .4139002
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .2871154{col 26}{space 2} .1069441{col 37}{space 1}    2.68{col 46}{space 3}0.008{col 54}{space 4}  .075017{col 67}{space 3} .4992139
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1816842{col 26}{space 2}  .056783{col 37}{space 1}   -3.20{col 46}{space 3}0.002{col 54}{space 4}   -.2943{col 67}{space 3}-.0690685
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=. & voted_nd_cum>0.5, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     8,596
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       429
{txt}{col 49}{help j_robustsingular##|_new:F(  76,    139)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1645
{txt}{col 49}Adj R-squared{col 67}= {res}    0.1123
{txt}{col 49}Root MSE{col 67}= {res}    0.4711

{txt}{ralign 78:(Std. Err. adjusted for {res:140} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0186569{col 26}{space 2} .0070774{col 37}{space 1}    2.64{col 46}{space 3}0.009{col 54}{space 4} .0046637{col 67}{space 3} .0326501
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0164295{col 26}{space 2} .0104336{col 37}{space 1}    1.57{col 46}{space 3}0.118{col 54}{space 4}-.0041995{col 67}{space 3} .0370586
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0169929{col 26}{space 2}   .00161{col 37}{space 1}   10.55{col 46}{space 3}0.000{col 54}{space 4} .0138096{col 67}{space 3} .0201761
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001961{col 26}{space 2} .0000192{col 37}{space 1}  -10.22{col 46}{space 3}0.000{col 54}{space 4}-.0002341{col 67}{space 3}-.0001582
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0080422{col 26}{space 2} .0127615{col 37}{space 1}    0.63{col 46}{space 3}0.530{col 54}{space 4}-.0171896{col 67}{space 3} .0332741
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0641715{col 26}{space 2} .0241993{col 37}{space 1}    2.65{col 46}{space 3}0.009{col 54}{space 4} .0163252{col 67}{space 3} .1120178
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0222876{col 26}{space 2} .0111241{col 37}{space 1}    2.00{col 46}{space 3}0.047{col 54}{space 4} .0002932{col 67}{space 3}  .044282
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0721478{col 26}{space 2} .0121222{col 37}{space 1}    5.95{col 46}{space 3}0.000{col 54}{space 4} .0481802{col 67}{space 3} .0961155
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2}  .007542{col 26}{space 2} .0943866{col 37}{space 1}    0.08{col 46}{space 3}0.936{col 54}{space 4}-.1790771{col 67}{space 3} .1941611
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2957726{col 26}{space 2} .2785458{col 37}{space 1}    1.06{col 46}{space 3}0.290{col 54}{space 4} -.254962{col 67}{space 3} .8465072
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .4747599{col 26}{space 2} .2894824{col 37}{space 1}    1.64{col 46}{space 3}0.103{col 54}{space 4}-.0975983{col 67}{space 3} 1.047118
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0385214{col 26}{space 2} .1326233{col 37}{space 1}    0.29{col 46}{space 3}0.772{col 54}{space 4}-.2236983{col 67}{space 3} .3007412
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2155094{col 26}{space 2} .0884206{col 37}{space 1}    2.44{col 46}{space 3}0.016{col 54}{space 4} .0406861{col 67}{space 3} .3903327
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .2746556{col 26}{space 2} .1004574{col 37}{space 1}    2.73{col 46}{space 3}0.007{col 54}{space 4} .0760334{col 67}{space 3} .4732778
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .2152609{col 26}{space 2} .1061043{col 37}{space 1}    2.03{col 46}{space 3}0.044{col 54}{space 4} .0054738{col 67}{space 3}  .425048
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .1909204{col 26}{space 2} .0917083{col 37}{space 1}    2.08{col 46}{space 3}0.039{col 54}{space 4} .0095967{col 67}{space 3}  .372244
{txt}{space 9}10  {c |}{col 14}{res}{space 2}    .2444{col 26}{space 2} .0916684{col 37}{space 1}    2.67{col 46}{space 3}0.009{col 54}{space 4} .0631552{col 67}{space 3} .4256448
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .2694161{col 26}{space 2}  .189259{col 37}{space 1}    1.42{col 46}{space 3}0.157{col 54}{space 4}-.1047826{col 67}{space 3} .6436148
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .4236666{col 26}{space 2}  .167073{col 37}{space 1}    2.54{col 46}{space 3}0.012{col 54}{space 4} .0933335{col 67}{space 3} .7539997
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .4716313{col 26}{space 2} .1219434{col 37}{space 1}    3.87{col 46}{space 3}0.000{col 54}{space 4} .2305275{col 67}{space 3} .7127351
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .4887652{col 26}{space 2} .1126291{col 37}{space 1}    4.34{col 46}{space 3}0.000{col 54}{space 4} .2660774{col 67}{space 3} .7114529
{txt}{space 9}15  {c |}{col 14}{res}{space 2}  .454506{col 26}{space 2} .1078729{col 37}{space 1}    4.21{col 46}{space 3}0.000{col 54}{space 4} .2412222{col 67}{space 3} .6677899
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .5888036{col 26}{space 2} .0929907{col 37}{space 1}    6.33{col 46}{space 3}0.000{col 54}{space 4} .4049445{col 67}{space 3} .7726628
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .5784771{col 26}{space 2} .0925236{col 37}{space 1}    6.25{col 46}{space 3}0.000{col 54}{space 4} .3955415{col 67}{space 3} .7614128
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{txt}{space 9}19  {c |}{col 14}{res}{space 2} .4850238{col 26}{space 2}  .114461{col 37}{space 1}    4.24{col 46}{space 3}0.000{col 54}{space 4}  .258714{col 67}{space 3} .7113335
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{txt}{space 9}36  {c |}{col 14}{res}{space 2} .5126141{col 26}{space 2} .1171534{col 37}{space 1}    4.38{col 46}{space 3}0.000{col 54}{space 4} .2809811{col 67}{space 3} .7442471
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .4639482{col 26}{space 2} .1161552{col 37}{space 1}    3.99{col 46}{space 3}0.000{col 54}{space 4} .2342887{col 67}{space 3} .6936078
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .5075126{col 26}{space 2} .1028642{col 37}{space 1}    4.93{col 46}{space 3}0.000{col 54}{space 4} .3041317{col 67}{space 3} .7108934
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .4540937{col 26}{space 2} .0981154{col 37}{space 1}    4.63{col 46}{space 3}0.000{col 54}{space 4} .2601022{col 67}{space 3} .6480852
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{txt}{space 9}45  {c |}{col 14}{res}{space 2} .3618837{col 26}{space 2}  .286635{col 37}{space 1}    1.26{col 46}{space 3}0.209{col 54}{space 4}-.2048446{col 67}{space 3} .9286119
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .2540593{col 26}{space 2} .1654843{col 37}{space 1}    1.54{col 46}{space 3}0.127{col 54}{space 4}-.0731327{col 67}{space 3} .5812512
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .8452067{col 26}{space 2} .1499409{col 37}{space 1}    5.64{col 46}{space 3}0.000{col 54}{space 4} .5487469{col 67}{space 3} 1.141667
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .2752872{col 26}{space 2}  .157554{col 37}{space 1}    1.75{col 46}{space 3}0.083{col 54}{space 4} -.036225{col 67}{space 3} .5867994
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3106829{col 26}{space 2} .1040439{col 37}{space 1}   -2.99{col 46}{space 3}0.003{col 54}{space 4}-.5163962{col 67}{space 3}-.1049697
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_ndlarge.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_ndlarge.tex"'})

{com}. 
. eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_nd_cum<0.5, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    30,665
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       495
{txt}{col 49}F({res}  77{txt},{res}    134{txt}){col 67}= {res}    283.62
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1037
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0867
{txt}{col 49}Root MSE{col 67}= {res}    0.4777

{txt}{ralign 78:(Std. Err. adjusted for {res:135} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0132888{col 26}{space 2} .0058322{col 37}{space 1}    2.28{col 46}{space 3}0.024{col 54}{space 4} .0017537{col 67}{space 3} .0248238
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0196255{col 26}{space 2} .0072529{col 37}{space 1}    2.71{col 46}{space 3}0.008{col 54}{space 4} .0052806{col 67}{space 3} .0339705
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0191316{col 26}{space 2} .0021112{col 37}{space 1}    9.06{col 46}{space 3}0.000{col 54}{space 4} .0149561{col 67}{space 3} .0233071
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002249{col 26}{space 2} .0000243{col 37}{space 1}   -9.25{col 46}{space 3}0.000{col 54}{space 4} -.000273{col 67}{space 3}-.0001768
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0158725{col 26}{space 2}   .00745{col 37}{space 1}    2.13{col 46}{space 3}0.035{col 54}{space 4} .0011376{col 67}{space 3} .0306073
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0522925{col 26}{space 2}  .012397{col 37}{space 1}    4.22{col 46}{space 3}0.000{col 54}{space 4} .0277733{col 67}{space 3} .0768116
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0146798{col 26}{space 2} .0049846{col 37}{space 1}    2.95{col 46}{space 3}0.004{col 54}{space 4} .0048212{col 67}{space 3} .0245384
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0785867{col 26}{space 2} .0083931{col 37}{space 1}    9.36{col 46}{space 3}0.000{col 54}{space 4} .0619866{col 67}{space 3} .0951868
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}65  {c |}{col 14}{res}{space 2}  .024576{col 26}{space 2} .3601128{col 37}{space 1}    0.07{col 46}{space 3}0.946{col 54}{space 4}-.6876644{col 67}{space 3} .7368163
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .3770777{col 26}{space 2} .3821276{col 37}{space 1}    0.99{col 46}{space 3}0.326{col 54}{space 4}-.3787041{col 67}{space 3}  1.13286
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .1510486{col 26}{space 2} .3440051{col 37}{space 1}    0.44{col 46}{space 3}0.661{col 54}{space 4}-.5293336{col 67}{space 3} .8314308
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .2991895{col 26}{space 2} .3558894{col 37}{space 1}    0.84{col 46}{space 3}0.402{col 54}{space 4}-.4046977{col 67}{space 3} 1.003077
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .2400502{col 26}{space 2} .3742492{col 37}{space 1}    0.64{col 46}{space 3}0.522{col 54}{space 4}-.5001495{col 67}{space 3} .9802499
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .1875453{col 26}{space 2} .3527294{col 37}{space 1}    0.53{col 46}{space 3}0.596{col 54}{space 4}-.5100919{col 67}{space 3} .8851826
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.0988184{col 26}{space 2} .3694224{col 37}{space 1}   -0.27{col 46}{space 3}0.789{col 54}{space 4}-.8294715{col 67}{space 3} .6318346
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum<0.25 & voted_nd_cum<0.5, absorb(Forsamling) vce(cluster Kommun)
{txt}note: 7.cohortbycountry omitted because of collinearity
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     8,971
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       400
{txt}{col 49}{help j_robustsingular##|_new:F(  71,    132)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1412
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0935
{txt}{col 49}Root MSE{col 67}= {res}    0.4739

{txt}{ralign 78:(Std. Err. adjusted for {res:133} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0004241{col 26}{space 2} .0181579{col 37}{space 1}    0.02{col 46}{space 3}0.981{col 54}{space 4}-.0354941{col 67}{space 3} .0363423
{txt}{space 9}sex {c |}{col 14}{res}{space 2}  .021092{col 26}{space 2} .0114902{col 37}{space 1}    1.84{col 46}{space 3}0.069{col 54}{space 4}-.0016368{col 67}{space 3} .0438208
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0257611{col 26}{space 2} .0023799{col 37}{space 1}   10.82{col 46}{space 3}0.000{col 54}{space 4} .0210535{col 67}{space 3} .0304688
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0003108{col 26}{space 2} .0000294{col 37}{space 1}  -10.56{col 46}{space 3}0.000{col 54}{space 4}-.0003691{col 67}{space 3}-.0002526
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0352826{col 26}{space 2} .0144773{col 37}{space 1}    2.44{col 46}{space 3}0.016{col 54}{space 4} .0066451{col 67}{space 3} .0639201
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0321502{col 26}{space 2} .0201928{col 37}{space 1}    1.59{col 46}{space 3}0.114{col 54}{space 4}-.0077932{col 67}{space 3} .0720936
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0150294{col 26}{space 2} .0070639{col 37}{space 1}    2.13{col 46}{space 3}0.035{col 54}{space 4} .0010563{col 67}{space 3} .0290025
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0496965{col 26}{space 2} .0136296{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .0227357{col 67}{space 3} .0766573
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}3  {c |}{col 14}{res}{space 2}-.8303185{col 26}{space 2}   .38937{col 37}{space 1}   -2.13{col 46}{space 3}0.035{col 54}{space 4}-1.600531{col 67}{space 3}-.0601062
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.7355409{col 26}{space 2} .0270589{col 37}{space 1}  -27.18{col 46}{space 3}0.000{col 54}{space 4} -.789066{col 67}{space 3}-.6820157
{txt}{space 10}7  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 10}8  {c |}{col 14}{res}{space 2}-.8839948{col 26}{space 2} .0363373{col 37}{space 1}  -24.33{col 46}{space 3}0.000{col 54}{space 4}-.9558736{col 67}{space 3}-.8121161
{txt}{space 10}9  {c |}{col 14}{res}{space 2}-.5599518{col 26}{space 2} .1496117{col 37}{space 1}   -3.74{col 46}{space 3}0.000{col 54}{space 4}-.8558985{col 67}{space 3} -.264005
{txt}{space 9}10  {c |}{col 14}{res}{space 2}-.5152777{col 26}{space 2} .0741111{col 37}{space 1}   -6.95{col 46}{space 3}0.000{col 54}{space 4}-.6618768{col 67}{space 3}-.3686786
{txt}{space 9}11  {c |}{col 14}{res}{space 2}-.2413657{col 26}{space 2} .1335209{col 37}{space 1}   -1.81{col 46}{space 3}0.073{col 54}{space 4}-.5054832{col 67}{space 3} .0227518
{txt}{space 9}12  {c |}{col 14}{res}{space 2}-.2306688{col 26}{space 2}  .111569{col 37}{space 1}   -2.07{col 46}{space 3}0.041{col 54}{space 4}-.4513633{col 67}{space 3}-.0099742
{txt}{space 9}13  {c |}{col 14}{res}{space 2}-.3088024{col 26}{space 2} .0996267{col 37}{space 1}   -3.10{col 46}{space 3}0.002{col 54}{space 4} -.505874{col 67}{space 3}-.1117308
{txt}{space 9}14  {c |}{col 14}{res}{space 2} -.383346{col 26}{space 2} .0780537{col 37}{space 1}   -4.91{col 46}{space 3}0.000{col 54}{space 4}-.5377439{col 67}{space 3}-.2289481
{txt}{space 9}15  {c |}{col 14}{res}{space 2}-.4051942{col 26}{space 2} .0889551{col 37}{space 1}   -4.56{col 46}{space 3}0.000{col 54}{space 4}-.5811562{col 67}{space 3}-.2292321
{txt}{space 9}16  {c |}{col 14}{res}{space 2}-.1790259{col 26}{space 2} .0484539{col 37}{space 1}   -3.69{col 46}{space 3}0.000{col 54}{space 4}-.2748724{col 67}{space 3}-.0831793
{txt}{space 9}17  {c |}{col 14}{res}{space 2}-.2547188{col 26}{space 2} .0443618{col 37}{space 1}   -5.74{col 46}{space 3}0.000{col 54}{space 4}-.3424709{col 67}{space 3}-.1669668
{txt}{space 9}18  {c |}{col 14}{res}{space 2}-.3248609{col 26}{space 2} .0448809{col 37}{space 1}   -7.24{col 46}{space 3}0.000{col 54}{space 4}-.4136397{col 67}{space 3}-.2360821
{txt}{space 9}19  {c |}{col 14}{res}{space 2}-.3345488{col 26}{space 2} .0647555{col 37}{space 1}   -5.17{col 46}{space 3}0.000{col 54}{space 4}-.4626415{col 67}{space 3} -.206456
{txt}{space 9}20  {c |}{col 14}{res}{space 2} -.566111{col 26}{space 2} .1273265{col 37}{space 1}   -4.45{col 46}{space 3}0.000{col 54}{space 4}-.8179754{col 67}{space 3}-.3142465
{txt}{space 9}21  {c |}{col 14}{res}{space 2}-.3701881{col 26}{space 2} .1567475{col 37}{space 1}   -2.36{col 46}{space 3}0.020{col 54}{space 4}-.6802502{col 67}{space 3} -.060126
{txt}{space 9}22  {c |}{col 14}{res}{space 2}-.4400341{col 26}{space 2} .1711127{col 37}{space 1}   -2.57{col 46}{space 3}0.011{col 54}{space 4} -.778512{col 67}{space 3}-.1015562
{txt}{space 9}23  {c |}{col 14}{res}{space 2}-.4364136{col 26}{space 2} .1516286{col 37}{space 1}   -2.88{col 46}{space 3}0.005{col 54}{space 4}-.7363499{col 67}{space 3}-.1364774
{txt}{space 9}24  {c |}{col 14}{res}{space 2}-.4566166{col 26}{space 2} .1228872{col 37}{space 1}   -3.72{col 46}{space 3}0.000{col 54}{space 4}-.6996996{col 67}{space 3}-.2135336
{txt}{space 9}25  {c |}{col 14}{res}{space 2}-.5822692{col 26}{space 2} .2000774{col 37}{space 1}   -2.91{col 46}{space 3}0.004{col 54}{space 4}-.9780421{col 67}{space 3}-.1864963
{txt}{space 9}26  {c |}{col 14}{res}{space 2}-.3585464{col 26}{space 2} .0337114{col 37}{space 1}  -10.64{col 46}{space 3}0.000{col 54}{space 4}-.4252307{col 67}{space 3} -.291862
{txt}{space 9}27  {c |}{col 14}{res}{space 2}-.3121794{col 26}{space 2} .0486809{col 37}{space 1}   -6.41{col 46}{space 3}0.000{col 54}{space 4} -.408475{col 67}{space 3}-.2158838
{txt}{space 9}28  {c |}{col 14}{res}{space 2}-.4133832{col 26}{space 2} .0314928{col 37}{space 1}  -13.13{col 46}{space 3}0.000{col 54}{space 4}-.4756791{col 67}{space 3}-.3510872
{txt}{space 9}29  {c |}{col 14}{res}{space 2} -.461786{col 26}{space 2} .0363573{col 37}{space 1}  -12.70{col 46}{space 3}0.000{col 54}{space 4}-.5337044{col 67}{space 3}-.3898675
{txt}{space 9}30  {c |}{col 14}{res}{space 2}-.5240289{col 26}{space 2} .0314172{col 37}{space 1}  -16.68{col 46}{space 3}0.000{col 54}{space 4}-.5861751{col 67}{space 3}-.4618826
{txt}{space 9}31  {c |}{col 14}{res}{space 2}-.2842778{col 26}{space 2} .0938303{col 37}{space 1}   -3.03{col 46}{space 3}0.003{col 54}{space 4}-.4698835{col 67}{space 3}-.0986721
{txt}{space 9}32  {c |}{col 14}{res}{space 2}-.3691852{col 26}{space 2} .0618084{col 37}{space 1}   -5.97{col 46}{space 3}0.000{col 54}{space 4}-.4914484{col 67}{space 3} -.246922
{txt}{space 9}33  {c |}{col 14}{res}{space 2}-.3659929{col 26}{space 2} .0390329{col 37}{space 1}   -9.38{col 46}{space 3}0.000{col 54}{space 4}-.4432038{col 67}{space 3}-.2887821
{txt}{space 9}34  {c |}{col 14}{res}{space 2}-.5489341{col 26}{space 2} .0307788{col 37}{space 1}  -17.83{col 46}{space 3}0.000{col 54}{space 4}-.6098176{col 67}{space 3}-.4880506
{txt}{space 9}35  {c |}{col 14}{res}{space 2}-.5263886{col 26}{space 2} .0589355{col 37}{space 1}   -8.93{col 46}{space 3}0.000{col 54}{space 4}-.6429688{col 67}{space 3}-.4098084
{txt}{space 9}36  {c |}{col 14}{res}{space 2}-.3992472{col 26}{space 2} .2178994{col 37}{space 1}   -1.83{col 46}{space 3}0.069{col 54}{space 4}-.8302738{col 67}{space 3} .0317793
{txt}{space 9}37  {c |}{col 14}{res}{space 2}-.3722819{col 26}{space 2} .2277404{col 37}{space 1}   -1.63{col 46}{space 3}0.104{col 54}{space 4}-.8227749{col 67}{space 3}  .078211
{txt}{space 9}38  {c |}{col 14}{res}{space 2}-.1544694{col 26}{space 2} .1104078{col 37}{space 1}   -1.40{col 46}{space 3}0.164{col 54}{space 4}-.3728668{col 67}{space 3} .0639281
{txt}{space 9}39  {c |}{col 14}{res}{space 2}-.5185631{col 26}{space 2} .1963746{col 37}{space 1}   -2.64{col 46}{space 3}0.009{col 54}{space 4}-.9070114{col 67}{space 3}-.1301148
{txt}{space 9}40  {c |}{col 14}{res}{space 2} -.230347{col 26}{space 2} .1292193{col 37}{space 1}   -1.78{col 46}{space 3}0.077{col 54}{space 4}-.4859556{col 67}{space 3} .0252616
{txt}{space 9}41  {c |}{col 14}{res}{space 2} -.314862{col 26}{space 2} .0324654{col 37}{space 1}   -9.70{col 46}{space 3}0.000{col 54}{space 4}-.3790817{col 67}{space 3}-.2506424
{txt}{space 9}42  {c |}{col 14}{res}{space 2}-.2721285{col 26}{space 2} .0245324{col 37}{space 1}  -11.09{col 46}{space 3}0.000{col 54}{space 4}-.3206559{col 67}{space 3} -.223601
{txt}{space 9}43  {c |}{col 14}{res}{space 2}-.3998665{col 26}{space 2} .0254313{col 37}{space 1}  -15.72{col 46}{space 3}0.000{col 54}{space 4}-.4501722{col 67}{space 3}-.3495608
{txt}{space 9}44  {c |}{col 14}{res}{space 2}-.3774639{col 26}{space 2} .0330057{col 37}{space 1}  -11.44{col 46}{space 3}0.000{col 54}{space 4}-.4427525{col 67}{space 3}-.3121754
{txt}{space 9}45  {c |}{col 14}{res}{space 2}-.3875432{col 26}{space 2} .0395377{col 37}{space 1}   -9.80{col 46}{space 3}0.000{col 54}{space 4}-.4657526{col 67}{space 3}-.3093338
{txt}{space 9}46  {c |}{col 14}{res}{space 2}-.2136599{col 26}{space 2} .0625907{col 37}{space 1}   -3.41{col 46}{space 3}0.001{col 54}{space 4}-.3374704{col 67}{space 3}-.0898493
{txt}{space 9}47  {c |}{col 14}{res}{space 2}-.2884561{col 26}{space 2} .0502499{col 37}{space 1}   -5.74{col 46}{space 3}0.000{col 54}{space 4}-.3878553{col 67}{space 3}-.1890569
{txt}{space 9}48  {c |}{col 14}{res}{space 2}-.3436675{col 26}{space 2} .0432028{col 37}{space 1}   -7.95{col 46}{space 3}0.000{col 54}{space 4} -.429127{col 67}{space 3} -.258208
{txt}{space 9}49  {c |}{col 14}{res}{space 2}-.3614209{col 26}{space 2} .0373248{col 37}{space 1}   -9.68{col 46}{space 3}0.000{col 54}{space 4} -.435253{col 67}{space 3}-.2875889
{txt}{space 9}50  {c |}{col 14}{res}{space 2} -.423002{col 26}{space 2} .0414782{col 37}{space 1}  -10.20{col 46}{space 3}0.000{col 54}{space 4}  -.50505{col 67}{space 3} -.340954
{txt}{space 9}51  {c |}{col 14}{res}{space 2}-.2088578{col 26}{space 2} .1431334{col 37}{space 1}   -1.46{col 46}{space 3}0.147{col 54}{space 4}-.4919898{col 67}{space 3} .0742741
{txt}{space 9}52  {c |}{col 14}{res}{space 2}-.2194822{col 26}{space 2} .0942706{col 37}{space 1}   -2.33{col 46}{space 3}0.021{col 54}{space 4}-.4059587{col 67}{space 3}-.0330056
{txt}{space 9}53  {c |}{col 14}{res}{space 2}-.3389556{col 26}{space 2} .0964385{col 37}{space 1}   -3.51{col 46}{space 3}0.001{col 54}{space 4}-.5297204{col 67}{space 3}-.1481907
{txt}{space 9}54  {c |}{col 14}{res}{space 2}-.3885118{col 26}{space 2} .0727979{col 37}{space 1}   -5.34{col 46}{space 3}0.000{col 54}{space 4}-.5325133{col 67}{space 3}-.2445103
{txt}{space 9}55  {c |}{col 14}{res}{space 2}-.2851502{col 26}{space 2} .0775561{col 37}{space 1}   -3.68{col 46}{space 3}0.000{col 54}{space 4}-.4385639{col 67}{space 3}-.1317365
{txt}{space 9}56  {c |}{col 14}{res}{space 2}-.4986347{col 26}{space 2} .0544996{col 37}{space 1}   -9.15{col 46}{space 3}0.000{col 54}{space 4}-.6064403{col 67}{space 3} -.390829
{txt}{space 9}57  {c |}{col 14}{res}{space 2}-.4017043{col 26}{space 2} .1300437{col 37}{space 1}   -3.09{col 46}{space 3}0.002{col 54}{space 4}-.6589435{col 67}{space 3}-.1444651
{txt}{space 9}58  {c |}{col 14}{res}{space 2}-.5949919{col 26}{space 2} .0530649{col 37}{space 1}  -11.21{col 46}{space 3}0.000{col 54}{space 4}-.6999595{col 67}{space 3}-.4900244
{txt}{space 9}59  {c |}{col 14}{res}{space 2}-.4448067{col 26}{space 2} .0989147{col 37}{space 1}   -4.50{col 46}{space 3}0.000{col 54}{space 4}-.6404697{col 67}{space 3}-.2491437
{txt}{space 9}60  {c |}{col 14}{res}{space 2}-.5160436{col 26}{space 2} .1021479{col 37}{space 1}   -5.05{col 46}{space 3}0.000{col 54}{space 4}-.7181022{col 67}{space 3}-.3139849
{txt}{space 9}61  {c |}{col 14}{res}{space 2}-.5942533{col 26}{space 2} .1132174{col 37}{space 1}   -5.25{col 46}{space 3}0.000{col 54}{space 4}-.8182085{col 67}{space 3}-.3702982
{txt}{space 9}62  {c |}{col 14}{res}{space 2}-.4102518{col 26}{space 2}  .144984{col 37}{space 1}   -2.83{col 46}{space 3}0.005{col 54}{space 4}-.6970445{col 67}{space 3}-.1234591
{txt}{space 9}63  {c |}{col 14}{res}{space 2}-.5864724{col 26}{space 2} .0646414{col 37}{space 1}   -9.07{col 46}{space 3}0.000{col 54}{space 4}-.7143394{col 67}{space 3}-.4586054
{txt}{space 9}64  {c |}{col 14}{res}{space 2}-.4591894{col 26}{space 2} .0775181{col 37}{space 1}   -5.92{col 46}{space 3}0.000{col 54}{space 4}-.6125279{col 67}{space 3}-.3058508
{txt}{space 9}65  {c |}{col 14}{res}{space 2}-.5127622{col 26}{space 2}  .068176{col 37}{space 1}   -7.52{col 46}{space 3}0.000{col 54}{space 4}-.6476211{col 67}{space 3}-.3779034
{txt}{space 9}66  {c |}{col 14}{res}{space 2} -.277615{col 26}{space 2} .1784547{col 37}{space 1}   -1.56{col 46}{space 3}0.122{col 54}{space 4} -.630616{col 67}{space 3}  .075386
{txt}{space 9}67  {c |}{col 14}{res}{space 2} -.510084{col 26}{space 2} .1351579{col 37}{space 1}   -3.77{col 46}{space 3}0.000{col 54}{space 4}-.7774397{col 67}{space 3}-.2427282
{txt}{space 9}68  {c |}{col 14}{res}{space 2}-.2232496{col 26}{space 2} .0968582{col 37}{space 1}   -2.30{col 46}{space 3}0.023{col 54}{space 4}-.4148446{col 67}{space 3}-.0316546
{txt}{space 9}69  {c |}{col 14}{res}{space 2}-.3472276{col 26}{space 2} .1334337{col 37}{space 1}   -2.60{col 46}{space 3}0.010{col 54}{space 4}-.6111727{col 67}{space 3}-.0832826
{txt}{space 9}70  {c |}{col 14}{res}{space 2}-.3439484{col 26}{space 2} .1173929{col 37}{space 1}   -2.93{col 46}{space 3}0.004{col 54}{space 4}-.5761631{col 67}{space 3}-.1117338
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .3799496{col 26}{space 2} .0435785{col 37}{space 1}    8.72{col 46}{space 3}0.000{col 54}{space 4} .2937471{col 67}{space 3} .4661522
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50 & voted_nd_cum<0.5 , absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     7,677
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       367
{txt}{col 49}{help j_robustsingular##|_new:F(  67,    130)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1460
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0942
{txt}{col 49}Root MSE{col 67}= {res}    0.4751

{txt}{ralign 78:(Std. Err. adjusted for {res:131} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}  .008476{col 26}{space 2} .0206684{col 37}{space 1}    0.41{col 46}{space 3}0.682{col 54}{space 4}-.0324138{col 67}{space 3} .0493659
{txt}{space 9}sex {c |}{col 14}{res}{space 2}  .039188{col 26}{space 2} .0122575{col 37}{space 1}    3.20{col 46}{space 3}0.002{col 54}{space 4}  .014938{col 67}{space 3} .0634379
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .021416{col 26}{space 2} .0034193{col 37}{space 1}    6.26{col 46}{space 3}0.000{col 54}{space 4} .0146513{col 67}{space 3} .0281807
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002595{col 26}{space 2} .0000406{col 37}{space 1}   -6.39{col 46}{space 3}0.000{col 54}{space 4}-.0003399{col 67}{space 3}-.0001792
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0436846{col 26}{space 2} .0189098{col 37}{space 1}    2.31{col 46}{space 3}0.022{col 54}{space 4} .0062738{col 67}{space 3} .0810954
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0490403{col 26}{space 2} .0256743{col 37}{space 1}    1.91{col 46}{space 3}0.058{col 54}{space 4}-.0017532{col 67}{space 3} .0998338
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0093426{col 26}{space 2} .0087343{col 37}{space 1}    1.07{col 46}{space 3}0.287{col 54}{space 4}-.0079371{col 67}{space 3} .0266223
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0865119{col 26}{space 2} .0162284{col 37}{space 1}    5.33{col 46}{space 3}0.000{col 54}{space 4}  .054406{col 67}{space 3} .1186178
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}4  {c |}{col 14}{res}{space 2} .3228358{col 26}{space 2} .0438497{col 37}{space 1}    7.36{col 46}{space 3}0.000{col 54}{space 4} .2360843{col 67}{space 3} .4095872
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .4620862{col 26}{space 2} .0651237{col 37}{space 1}    7.10{col 46}{space 3}0.000{col 54}{space 4} .3332468{col 67}{space 3} .5909257
{txt}{space 10}8  {c |}{col 14}{res}{space 2} 1.411936{col 26}{space 2} .0606806{col 37}{space 1}   23.27{col 46}{space 3}0.000{col 54}{space 4} 1.291887{col 67}{space 3} 1.531985
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3385649{col 26}{space 2} .1074879{col 37}{space 1}    3.15{col 46}{space 3}0.002{col 54}{space 4}  .125913{col 67}{space 3} .5512168
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .5336646{col 26}{space 2} .2236043{col 37}{space 1}    2.39{col 46}{space 3}0.018{col 54}{space 4} .0912903{col 67}{space 3} .9760389
{txt}{space 9}11  {c |}{col 14}{res}{space 2} 1.357342{col 26}{space 2} .0723895{col 37}{space 1}   18.75{col 46}{space 3}0.000{col 54}{space 4} 1.214128{col 67}{space 3} 1.500556
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .8860853{col 26}{space 2} .0873209{col 37}{space 1}   10.15{col 46}{space 3}0.000{col 54}{space 4} .7133314{col 67}{space 3} 1.058839
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .9408531{col 26}{space 2} .1123871{col 37}{space 1}    8.37{col 46}{space 3}0.000{col 54}{space 4} .7185086{col 67}{space 3} 1.163198
{txt}{space 9}14  {c |}{col 14}{res}{space 2}   .93749{col 26}{space 2}  .104302{col 37}{space 1}    8.99{col 46}{space 3}0.000{col 54}{space 4} .7311408{col 67}{space 3} 1.143839
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .7713682{col 26}{space 2} .1841302{col 37}{space 1}    4.19{col 46}{space 3}0.000{col 54}{space 4} .4070886{col 67}{space 3} 1.135648
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .8557296{col 26}{space 2} .0877078{col 37}{space 1}    9.76{col 46}{space 3}0.000{col 54}{space 4} .6822101{col 67}{space 3} 1.029249
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .8984678{col 26}{space 2} .0747524{col 37}{space 1}   12.02{col 46}{space 3}0.000{col 54}{space 4}  .750579{col 67}{space 3} 1.046357
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .8747247{col 26}{space 2} .0615308{col 37}{space 1}   14.22{col 46}{space 3}0.000{col 54}{space 4} .7529934{col 67}{space 3}  .996456
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .8657249{col 26}{space 2} .0850346{col 37}{space 1}   10.18{col 46}{space 3}0.000{col 54}{space 4} .6974941{col 67}{space 3} 1.033956
{txt}{space 9}20  {c |}{col 14}{res}{space 2} 1.105397{col 26}{space 2} .1142391{col 37}{space 1}    9.68{col 46}{space 3}0.000{col 54}{space 4} .8793885{col 67}{space 3} 1.331405
{txt}{space 9}21  {c |}{col 14}{res}{space 2} 1.225412{col 26}{space 2} .1478267{col 37}{space 1}    8.29{col 46}{space 3}0.000{col 54}{space 4} .9329542{col 67}{space 3} 1.517869
{txt}{space 9}22  {c |}{col 14}{res}{space 2} 1.189142{col 26}{space 2} .0725254{col 37}{space 1}   16.40{col 46}{space 3}0.000{col 54}{space 4} 1.045659{col 67}{space 3} 1.332625
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .9339244{col 26}{space 2} .1940017{col 37}{space 1}    4.81{col 46}{space 3}0.000{col 54}{space 4} .5501153{col 67}{space 3} 1.317734
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .6281344{col 26}{space 2} .1338867{col 37}{space 1}    4.69{col 46}{space 3}0.000{col 54}{space 4} .3632556{col 67}{space 3} .8930133
{txt}{space 9}25  {c |}{col 14}{res}{space 2}  .512337{col 26}{space 2} .1299436{col 37}{space 1}    3.94{col 46}{space 3}0.000{col 54}{space 4} .2552592{col 67}{space 3} .7694148
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .7572918{col 26}{space 2} .0540736{col 37}{space 1}   14.00{col 46}{space 3}0.000{col 54}{space 4} .6503137{col 67}{space 3} .8642698
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .8968088{col 26}{space 2} .0609437{col 37}{space 1}   14.72{col 46}{space 3}0.000{col 54}{space 4}  .776239{col 67}{space 3} 1.017379
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .7875764{col 26}{space 2} .0661636{col 37}{space 1}   11.90{col 46}{space 3}0.000{col 54}{space 4} .6566797{col 67}{space 3} .9184731
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .7018297{col 26}{space 2} .0538601{col 37}{space 1}   13.03{col 46}{space 3}0.000{col 54}{space 4}  .595274{col 67}{space 3} .8083854
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .6410608{col 26}{space 2}  .049484{col 37}{space 1}   12.95{col 46}{space 3}0.000{col 54}{space 4} .5431626{col 67}{space 3}  .738959
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .9701026{col 26}{space 2} .1017448{col 37}{space 1}    9.53{col 46}{space 3}0.000{col 54}{space 4} .7688126{col 67}{space 3} 1.171393
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .8579511{col 26}{space 2} .1051982{col 37}{space 1}    8.16{col 46}{space 3}0.000{col 54}{space 4} .6498289{col 67}{space 3} 1.066073
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .7772893{col 26}{space 2} .0501846{col 37}{space 1}   15.49{col 46}{space 3}0.000{col 54}{space 4} .6780051{col 67}{space 3} .8765735
{txt}{space 9}34  {c |}{col 14}{res}{space 2}  .604236{col 26}{space 2} .0394579{col 37}{space 1}   15.31{col 46}{space 3}0.000{col 54}{space 4} .5261733{col 67}{space 3} .6822986
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .7175804{col 26}{space 2} .0720386{col 37}{space 1}    9.96{col 46}{space 3}0.000{col 54}{space 4} .5750607{col 67}{space 3} .8601001
{txt}{space 9}36  {c |}{col 14}{res}{space 2}  1.09227{col 26}{space 2} .0445788{col 37}{space 1}   24.50{col 46}{space 3}0.000{col 54}{space 4} 1.004076{col 67}{space 3} 1.180464
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .5137705{col 26}{space 2} .2650336{col 37}{space 1}    1.94{col 46}{space 3}0.055{col 54}{space 4}-.0105667{col 67}{space 3} 1.038108
{txt}{space 9}38  {c |}{col 14}{res}{space 2} .7241411{col 26}{space 2} .1414845{col 37}{space 1}    5.12{col 46}{space 3}0.000{col 54}{space 4} .4442309{col 67}{space 3} 1.004051
{txt}{space 9}39  {c |}{col 14}{res}{space 2} .9591279{col 26}{space 2}  .153035{col 37}{space 1}    6.27{col 46}{space 3}0.000{col 54}{space 4} .6563665{col 67}{space 3} 1.261889
{txt}{space 9}40  {c |}{col 14}{res}{space 2} 1.041083{col 26}{space 2} .1788794{col 37}{space 1}    5.82{col 46}{space 3}0.000{col 54}{space 4} .6871919{col 67}{space 3} 1.394975
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .8284308{col 26}{space 2} .0503288{col 37}{space 1}   16.46{col 46}{space 3}0.000{col 54}{space 4} .7288613{col 67}{space 3} .9280003
{txt}{space 9}42  {c |}{col 14}{res}{space 2} .8628438{col 26}{space 2} .0457646{col 37}{space 1}   18.85{col 46}{space 3}0.000{col 54}{space 4} .7723041{col 67}{space 3} .9533836
{txt}{space 9}43  {c |}{col 14}{res}{space 2} .7953925{col 26}{space 2} .0390769{col 37}{space 1}   20.35{col 46}{space 3}0.000{col 54}{space 4} .7180836{col 67}{space 3} .8727014
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .7596019{col 26}{space 2} .0388256{col 37}{space 1}   19.56{col 46}{space 3}0.000{col 54}{space 4} .6827901{col 67}{space 3} .8364137
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .6942988{col 26}{space 2} .0421771{col 37}{space 1}   16.46{col 46}{space 3}0.000{col 54}{space 4} .6108564{col 67}{space 3} .7777411
{txt}{space 9}46  {c |}{col 14}{res}{space 2} .6505911{col 26}{space 2} .0961802{col 37}{space 1}    6.76{col 46}{space 3}0.000{col 54}{space 4} .4603101{col 67}{space 3} .8408722
{txt}{space 9}47  {c |}{col 14}{res}{space 2} .7140573{col 26}{space 2} .0537951{col 37}{space 1}   13.27{col 46}{space 3}0.000{col 54}{space 4} .6076301{col 67}{space 3} .8204845
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .8100474{col 26}{space 2} .0618996{col 37}{space 1}   13.09{col 46}{space 3}0.000{col 54}{space 4} .6875863{col 67}{space 3} .9325084
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .7144888{col 26}{space 2}  .045766{col 37}{space 1}   15.61{col 46}{space 3}0.000{col 54}{space 4} .6239462{col 67}{space 3} .8050314
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .7198056{col 26}{space 2} .0561542{col 37}{space 1}   12.82{col 46}{space 3}0.000{col 54}{space 4} .6087113{col 67}{space 3} .8308999
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .7115169{col 26}{space 2} .2765777{col 37}{space 1}    2.57{col 46}{space 3}0.011{col 54}{space 4} .1643411{col 67}{space 3} 1.258693
{txt}{space 9}52  {c |}{col 14}{res}{space 2}  1.18867{col 26}{space 2} .0675896{col 37}{space 1}   17.59{col 46}{space 3}0.000{col 54}{space 4} 1.054952{col 67}{space 3} 1.322388
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .7980077{col 26}{space 2} .1309272{col 37}{space 1}    6.10{col 46}{space 3}0.000{col 54}{space 4} .5389839{col 67}{space 3} 1.057031
{txt}{space 9}54  {c |}{col 14}{res}{space 2}   .84676{col 26}{space 2} .0879494{col 37}{space 1}    9.63{col 46}{space 3}0.000{col 54}{space 4} .6727626{col 67}{space 3} 1.020757
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .7540918{col 26}{space 2} .0714253{col 37}{space 1}   10.56{col 46}{space 3}0.000{col 54}{space 4} .6127854{col 67}{space 3} .8953981
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .5950366{col 26}{space 2} .2481085{col 37}{space 1}    2.40{col 46}{space 3}0.018{col 54}{space 4} .1041837{col 67}{space 3} 1.085889
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .5427717{col 26}{space 2} .1086441{col 37}{space 1}    5.00{col 46}{space 3}0.000{col 54}{space 4} .3278323{col 67}{space 3} .7577112
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .5989296{col 26}{space 2} .0980556{col 37}{space 1}    6.11{col 46}{space 3}0.000{col 54}{space 4} .4049382{col 67}{space 3}  .792921
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .5066851{col 26}{space 2}  .112037{col 37}{space 1}    4.52{col 46}{space 3}0.000{col 54}{space 4} .2850333{col 67}{space 3}  .728337
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .4790973{col 26}{space 2} .0781926{col 37}{space 1}    6.13{col 46}{space 3}0.000{col 54}{space 4} .3244025{col 67}{space 3}  .633792
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .7962813{col 26}{space 2} .1360588{col 37}{space 1}    5.85{col 46}{space 3}0.000{col 54}{space 4} .5271052{col 67}{space 3} 1.065457
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .7732528{col 26}{space 2} .0842638{col 37}{space 1}    9.18{col 46}{space 3}0.000{col 54}{space 4}  .606547{col 67}{space 3} .9399586
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .6958218{col 26}{space 2} .0518771{col 37}{space 1}   13.41{col 46}{space 3}0.000{col 54}{space 4} .5931892{col 67}{space 3} .7984544
{txt}{space 9}64  {c |}{col 14}{res}{space 2}  .764026{col 26}{space 2} .0806515{col 37}{space 1}    9.47{col 46}{space 3}0.000{col 54}{space 4} .6044668{col 67}{space 3} .9235853
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .6161715{col 26}{space 2} .0703525{col 37}{space 1}    8.76{col 46}{space 3}0.000{col 54}{space 4} .4769875{col 67}{space 3} .7553556
{txt}{space 9}66  {c |}{col 14}{res}{space 2}  .023174{col 26}{space 2} .0487276{col 37}{space 1}    0.48{col 46}{space 3}0.635{col 54}{space 4}-.0732278{col 67}{space 3} .1195757
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .8802577{col 26}{space 2} .1314914{col 37}{space 1}    6.69{col 46}{space 3}0.000{col 54}{space 4} .6201177{col 67}{space 3} 1.140398
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .9121574{col 26}{space 2} .0823628{col 37}{space 1}   11.07{col 46}{space 3}0.000{col 54}{space 4} .7492125{col 67}{space 3} 1.075102
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .8812413{col 26}{space 2} .1137218{col 37}{space 1}    7.75{col 46}{space 3}0.000{col 54}{space 4} .6562564{col 67}{space 3} 1.106226
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .8213843{col 26}{space 2} .0974001{col 37}{space 1}    8.43{col 46}{space 3}0.000{col 54}{space 4}   .62869{col 67}{space 3} 1.014079
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.7311188{col 26}{space 2} .0764414{col 37}{space 1}   -9.56{col 46}{space 3}0.000{col 54}{space 4}-.8823489{col 67}{space 3}-.5798887
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.  
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75 & voted_nd_cum<0.5, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     7,312
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       193
{txt}{col 49}{help j_robustsingular##|_new:F(  68,     76)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1163
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0835
{txt}{col 49}Root MSE{col 67}= {res}    0.4785

{txt}{ralign 78:(Std. Err. adjusted for {res:77} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0419228{col 26}{space 2} .0127589{col 37}{space 1}   -3.29{col 46}{space 3}0.002{col 54}{space 4}-.0673345{col 67}{space 3}-.0165112
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0100041{col 26}{space 2}  .012274{col 37}{space 1}    0.82{col 46}{space 3}0.418{col 54}{space 4}-.0144416{col 67}{space 3} .0344499
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0179739{col 26}{space 2} .0025226{col 37}{space 1}    7.13{col 46}{space 3}0.000{col 54}{space 4} .0129496{col 67}{space 3} .0229981
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002055{col 26}{space 2} .0000254{col 37}{space 1}   -8.10{col 46}{space 3}0.000{col 54}{space 4} -.000256{col 67}{space 3} -.000155
{txt}{space 5}married {c |}{col 14}{res}{space 2}-.0174796{col 26}{space 2} .0168904{col 37}{space 1}   -1.03{col 46}{space 3}0.304{col 54}{space 4}-.0511197{col 67}{space 3} .0161606
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0322324{col 26}{space 2} .0238249{col 37}{space 1}    1.35{col 46}{space 3}0.180{col 54}{space 4} -.015219{col 67}{space 3} .0796838
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0217512{col 26}{space 2} .0068088{col 37}{space 1}    3.19{col 46}{space 3}0.002{col 54}{space 4} .0081902{col 67}{space 3} .0353122
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2}  .082326{col 26}{space 2} .0151713{col 37}{space 1}    5.43{col 46}{space 3}0.000{col 54}{space 4} .0521097{col 67}{space 3} .1125423
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}9  {c |}{col 14}{res}{space 2} .0426872{col 26}{space 2} .3671191{col 37}{space 1}    0.12{col 46}{space 3}0.908{col 54}{space 4}-.6884938{col 67}{space 3} .7738682
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .0337823{col 26}{space 2} .5503505{col 37}{space 1}    0.06{col 46}{space 3}0.951{col 54}{space 4}-1.062336{col 67}{space 3}   1.1299
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4712496{col 26}{space 2} .5439857{col 37}{space 1}    0.87{col 46}{space 3}0.389{col 54}{space 4}-.6121918{col 67}{space 3} 1.554691
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .3884788{col 26}{space 2} .5683371{col 37}{space 1}    0.68{col 46}{space 3}0.496{col 54}{space 4}-.7434626{col 67}{space 3}  1.52042
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .0987318{col 26}{space 2} .5516567{col 37}{space 1}    0.18{col 46}{space 3}0.858{col 54}{space 4}-.9999878{col 67}{space 3} 1.197451
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .3902532{col 26}{space 2} .5730706{col 37}{space 1}    0.68{col 46}{space 3}0.498{col 54}{space 4}-.7511159{col 67}{space 3} 1.531622
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .2899024{col 26}{space 2} .5567829{col 37}{space 1}    0.52{col 46}{space 3}0.604{col 54}{space 4}-.8190268{col 67}{space 3} 1.398832
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .4053732{col 26}{space 2} .5433971{col 37}{space 1}    0.75{col 46}{space 3}0.458{col 54}{space 4}-.6768959{col 67}{space 3} 1.487642
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .3321028{col 26}{space 2}  .543869{col 37}{space 1}    0.61{col 46}{space 3}0.543{col 54}{space 4}-.7511062{col 67}{space 3} 1.415312
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .3822513{col 26}{space 2} .5397639{col 37}{space 1}    0.71{col 46}{space 3}0.481{col 54}{space 4}-.6927818{col 67}{space 3} 1.457284
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .1495058{col 26}{space 2} .5408553{col 37}{space 1}    0.28{col 46}{space 3}0.783{col 54}{space 4}-.9277008{col 67}{space 3} 1.226712
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .2338465{col 26}{space 2} .5418732{col 37}{space 1}    0.43{col 46}{space 3}0.667{col 54}{space 4}-.8453874{col 67}{space 3} 1.313081
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .2464094{col 26}{space 2} .5438134{col 37}{space 1}    0.45{col 46}{space 3}0.652{col 54}{space 4}-.8366889{col 67}{space 3} 1.329508
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .1975237{col 26}{space 2} .5522643{col 37}{space 1}    0.36{col 46}{space 3}0.722{col 54}{space 4}-.9024059{col 67}{space 3} 1.297453
{txt}{space 9}23  {c |}{col 14}{res}{space 2}  .424979{col 26}{space 2} .5508068{col 37}{space 1}    0.77{col 46}{space 3}0.443{col 54}{space 4}-.6720479{col 67}{space 3} 1.522006
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .2162771{col 26}{space 2} .5488711{col 37}{space 1}    0.39{col 46}{space 3}0.695{col 54}{space 4}-.8768945{col 67}{space 3} 1.309449
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{txt}{space 9}28  {c |}{col 14}{res}{space 2}  .229748{col 26}{space 2} .5402965{col 37}{space 1}    0.43{col 46}{space 3}0.672{col 54}{space 4}-.8463457{col 67}{space 3} 1.305842
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{txt}{space 9}31  {c |}{col 14}{res}{space 2} .2723118{col 26}{space 2}  .543189{col 37}{space 1}    0.50{col 46}{space 3}0.618{col 54}{space 4}-.8095428{col 67}{space 3} 1.354166
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{txt}{space 9}47  {c |}{col 14}{res}{space 2} .2718127{col 26}{space 2} .5414349{col 37}{space 1}    0.50{col 46}{space 3}0.617{col 54}{space 4}-.8065484{col 67}{space 3} 1.350174
{txt}{space 9}48  {c |}{col 14}{res}{space 2} .2440692{col 26}{space 2} .5420337{col 37}{space 1}    0.45{col 46}{space 3}0.654{col 54}{space 4}-.8354844{col 67}{space 3} 1.323623
{txt}{space 9}49  {c |}{col 14}{res}{space 2} .1944837{col 26}{space 2} .5444374{col 37}{space 1}    0.36{col 46}{space 3}0.722{col 54}{space 4}-.8898573{col 67}{space 3} 1.278825
{txt}{space 9}50  {c |}{col 14}{res}{space 2} .2813405{col 26}{space 2} .5488745{col 37}{space 1}    0.51{col 46}{space 3}0.610{col 54}{space 4}-.8118378{col 67}{space 3} 1.374519
{txt}{space 9}51  {c |}{col 14}{res}{space 2} .5826698{col 26}{space 2} .5612124{col 37}{space 1}    1.04{col 46}{space 3}0.302{col 54}{space 4}-.5350815{col 67}{space 3} 1.700421
{txt}{space 9}52  {c |}{col 14}{res}{space 2} .4249366{col 26}{space 2}  .556615{col 37}{space 1}    0.76{col 46}{space 3}0.448{col 54}{space 4}-.6836583{col 67}{space 3} 1.533532
{txt}{space 9}53  {c |}{col 14}{res}{space 2} .3108073{col 26}{space 2} .5439503{col 37}{space 1}    0.57{col 46}{space 3}0.569{col 54}{space 4}-.7725635{col 67}{space 3} 1.394178
{txt}{space 9}54  {c |}{col 14}{res}{space 2} .2944258{col 26}{space 2} .5469027{col 37}{space 1}    0.54{col 46}{space 3}0.592{col 54}{space 4}-.7948253{col 67}{space 3} 1.383677
{txt}{space 9}55  {c |}{col 14}{res}{space 2}  .428045{col 26}{space 2} .5448028{col 37}{space 1}    0.79{col 46}{space 3}0.434{col 54}{space 4}-.6570237{col 67}{space 3} 1.513114
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .0449173{col 26}{space 2} .5413753{col 37}{space 1}    0.08{col 46}{space 3}0.934{col 54}{space 4}-1.033325{col 67}{space 3}  1.12316
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .0184841{col 26}{space 2} .5410419{col 37}{space 1}    0.03{col 46}{space 3}0.973{col 54}{space 4}-1.059094{col 67}{space 3} 1.096062
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .1026508{col 26}{space 2} .5405361{col 37}{space 1}    0.19{col 46}{space 3}0.850{col 54}{space 4}-.9739201{col 67}{space 3} 1.179222
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .0134313{col 26}{space 2} .5418136{col 37}{space 1}    0.02{col 46}{space 3}0.980{col 54}{space 4}-1.065684{col 67}{space 3} 1.092547
{txt}{space 9}60  {c |}{col 14}{res}{space 2}-.0028057{col 26}{space 2} .5445622{col 37}{space 1}   -0.01{col 46}{space 3}0.996{col 54}{space 4}-1.087395{col 67}{space 3} 1.081784
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .4043222{col 26}{space 2} .5432652{col 37}{space 1}    0.74{col 46}{space 3}0.459{col 54}{space 4}-.6776842{col 67}{space 3} 1.486329
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .2833202{col 26}{space 2} .5426732{col 37}{space 1}    0.52{col 46}{space 3}0.603{col 54}{space 4}-.7975071{col 67}{space 3} 1.364147
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .2667868{col 26}{space 2} .5525954{col 37}{space 1}    0.48{col 46}{space 3}0.631{col 54}{space 4}-.8338023{col 67}{space 3} 1.367376
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .1477815{col 26}{space 2} .5461011{col 37}{space 1}    0.27{col 46}{space 3}0.787{col 54}{space 4}-.9398732{col 67}{space 3} 1.235436
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .1349099{col 26}{space 2} .5453246{col 37}{space 1}    0.25{col 46}{space 3}0.805{col 54}{space 4}-.9511981{col 67}{space 3} 1.221018
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .5977463{col 26}{space 2} .5479173{col 37}{space 1}    1.09{col 46}{space 3}0.279{col 54}{space 4}-.4935257{col 67}{space 3} 1.689018
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .2545016{col 26}{space 2} .5523231{col 37}{space 1}    0.46{col 46}{space 3}0.646{col 54}{space 4}-.8455453{col 67}{space 3} 1.354548
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .2611952{col 26}{space 2} .5435473{col 37}{space 1}    0.48{col 46}{space 3}0.632{col 54}{space 4} -.821373{col 67}{space 3} 1.343763
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .3418533{col 26}{space 2} .5429111{col 37}{space 1}    0.63{col 46}{space 3}0.531{col 54}{space 4}-.7394478{col 67}{space 3} 1.423154
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .2035463{col 26}{space 2} .5427807{col 37}{space 1}    0.38{col 46}{space 3}0.709{col 54}{space 4}-.8774953{col 67}{space 3} 1.284588
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.1676929{col 26}{space 2} .5394092{col 37}{space 1}   -0.31{col 46}{space 3}0.757{col 54}{space 4}-1.242019{col 67}{space 3} .9066336
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare sex age age2 married parent children high_educ i.cohortbycountry if voted_coethnics_cum>0.75 & voted_coethnics_cum!=. & voted_nd_cum<0.5, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}     6,705
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       348
{txt}{col 49}{help j_robustsingular##|_new:F(  76,    114)}{col 67}=          {res}.
{txt}{col 49}Prob > F{col 67}=          {res}.
{txt}{col 49}R-squared{col 67}= {res}    0.1458
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0881
{txt}{col 49}Root MSE{col 67}= {res}    0.4766

{txt}{ralign 78:(Std. Err. adjusted for {res:115} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0238855{col 26}{space 2} .0060216{col 37}{space 1}    3.97{col 46}{space 3}0.000{col 54}{space 4} .0119568{col 67}{space 3} .0358142
{txt}{space 9}sex {c |}{col 14}{res}{space 2}  .007591{col 26}{space 2} .0094064{col 37}{space 1}    0.81{col 46}{space 3}0.421{col 54}{space 4}-.0110429{col 67}{space 3} .0262249
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0136596{col 26}{space 2} .0029342{col 37}{space 1}    4.66{col 46}{space 3}0.000{col 54}{space 4} .0078469{col 67}{space 3} .0194722
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001616{col 26}{space 2} .0000335{col 37}{space 1}   -4.82{col 46}{space 3}0.000{col 54}{space 4}-.0002279{col 67}{space 3}-.0000952
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0103454{col 26}{space 2} .0161711{col 37}{space 1}    0.64{col 46}{space 3}0.524{col 54}{space 4}-.0216893{col 67}{space 3} .0423802
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .1080586{col 26}{space 2}  .041148{col 37}{space 1}    2.63{col 46}{space 3}0.010{col 54}{space 4} .0265447{col 67}{space 3} .1895725
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0064787{col 26}{space 2} .0133555{col 37}{space 1}    0.49{col 46}{space 3}0.629{col 54}{space 4}-.0199784{col 67}{space 3} .0329359
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2}  .084692{col 26}{space 2} .0112609{col 37}{space 1}    7.52{col 46}{space 3}0.000{col 54}{space 4} .0623843{col 67}{space 3} .1069997
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .6634522{col 26}{space 2} .3385239{col 37}{space 1}    1.96{col 46}{space 3}0.052{col 54}{space 4} -.007161{col 67}{space 3} 1.334065
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .5383219{col 26}{space 2} .2685483{col 37}{space 1}    2.00{col 46}{space 3}0.047{col 54}{space 4} .0063298{col 67}{space 3} 1.070314
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5695148{col 26}{space 2} .4234536{col 37}{space 1}    1.34{col 46}{space 3}0.181{col 54}{space 4}-.2693436{col 67}{space 3} 1.408373
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4979936{col 26}{space 2} .2671979{col 37}{space 1}    1.86{col 46}{space 3}0.065{col 54}{space 4}-.0313233{col 67}{space 3}  1.02731
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .4572041{col 26}{space 2} .0656137{col 37}{space 1}    6.97{col 46}{space 3}0.000{col 54}{space 4} .3272238{col 67}{space 3} .5871844
{txt}{space 10}7  {c |}{col 14}{res}{space 2}  .481707{col 26}{space 2} .0302745{col 37}{space 1}   15.91{col 46}{space 3}0.000{col 54}{space 4} .4217334{col 67}{space 3} .5416805
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .3881787{col 26}{space 2} .0419321{col 37}{space 1}    9.26{col 46}{space 3}0.000{col 54}{space 4} .3051114{col 67}{space 3} .4712459
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .3325998{col 26}{space 2} .0306475{col 37}{space 1}   10.85{col 46}{space 3}0.000{col 54}{space 4} .2718873{col 67}{space 3} .3933122
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .3290796{col 26}{space 2} .0480919{col 37}{space 1}    6.84{col 46}{space 3}0.000{col 54}{space 4} .2338099{col 67}{space 3} .4243493
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .6696814{col 26}{space 2} .0822699{col 37}{space 1}    8.14{col 46}{space 3}0.000{col 54}{space 4} .5067054{col 67}{space 3} .8326573
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .5040955{col 26}{space 2} .0672913{col 37}{space 1}    7.49{col 46}{space 3}0.000{col 54}{space 4}  .370792{col 67}{space 3}  .637399
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .7277818{col 26}{space 2} .1205076{col 37}{space 1}    6.04{col 46}{space 3}0.000{col 54}{space 4} .4890571{col 67}{space 3} .9665065
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .5940714{col 26}{space 2} .0666971{col 37}{space 1}    8.91{col 46}{space 3}0.000{col 54}{space 4} .4619449{col 67}{space 3} .7261979
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .5354753{col 26}{space 2} .0744086{col 37}{space 1}    7.20{col 46}{space 3}0.000{col 54}{space 4} .3880726{col 67}{space 3} .6828781
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .7090389{col 26}{space 2} .0392903{col 37}{space 1}   18.05{col 46}{space 3}0.000{col 54}{space 4} .6312052{col 67}{space 3} .7868726
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{txt}{space 9}25  {c |}{col 14}{res}{space 2} .6425859{col 26}{space 2} .0330063{col 37}{space 1}   19.47{col 46}{space 3}0.000{col 54}{space 4} .5772007{col 67}{space 3} .7079711
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .6610005{col 26}{space 2}  .037983{col 37}{space 1}   17.40{col 46}{space 3}0.000{col 54}{space 4} .5857566{col 67}{space 3} .7362445
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .5976379{col 26}{space 2}  .060695{col 37}{space 1}    9.85{col 46}{space 3}0.000{col 54}{space 4} .4774016{col 67}{space 3} .7178742
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .5574326{col 26}{space 2} .1696934{col 37}{space 1}    3.28{col 46}{space 3}0.001{col 54}{space 4} .2212712{col 67}{space 3}  .893594
{txt}{space 9}29  {c |}{col 14}{res}{space 2} .3896834{col 26}{space 2} .0844752{col 37}{space 1}    4.61{col 46}{space 3}0.000{col 54}{space 4} .2223387{col 67}{space 3} .5570282
{txt}{space 9}30  {c |}{col 14}{res}{space 2} .3986453{col 26}{space 2} .1236085{col 37}{space 1}    3.23{col 46}{space 3}0.002{col 54}{space 4} .1537778{col 67}{space 3} .6435127
{txt}{space 9}31  {c |}{col 14}{res}{space 2} .5066431{col 26}{space 2} .0535533{col 37}{space 1}    9.46{col 46}{space 3}0.000{col 54}{space 4} .4005544{col 67}{space 3} .6127318
{txt}{space 9}32  {c |}{col 14}{res}{space 2} .5576039{col 26}{space 2} .0221641{col 37}{space 1}   25.16{col 46}{space 3}0.000{col 54}{space 4} .5136971{col 67}{space 3} .6015108
{txt}{space 9}33  {c |}{col 14}{res}{space 2} .5552168{col 26}{space 2} .0351456{col 37}{space 1}   15.80{col 46}{space 3}0.000{col 54}{space 4} .4855937{col 67}{space 3}   .62484
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .4959205{col 26}{space 2} .0319432{col 37}{space 1}   15.53{col 46}{space 3}0.000{col 54}{space 4} .4326414{col 67}{space 3} .5591997
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .5026334{col 26}{space 2} .0257288{col 37}{space 1}   19.54{col 46}{space 3}0.000{col 54}{space 4} .4516648{col 67}{space 3}  .553602
{txt}{space 9}36  {c |}{col 14}{res}{space 2} .6143322{col 26}{space 2} .0360458{col 37}{space 1}   17.04{col 46}{space 3}0.000{col 54}{space 4} .5429258{col 67}{space 3} .6857386
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .5482433{col 26}{space 2} .0377365{col 37}{space 1}   14.53{col 46}{space 3}0.000{col 54}{space 4} .4734875{col 67}{space 3} .6229991
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3902668{col 26}{space 2} .0633704{col 37}{space 1}   -6.16{col 46}{space 3}0.000{col 54}{space 4}-.5158031{col 67}{space 3}-.2647306
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_ndsmall.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_ndsmall.tex"'})

{com}. 
. 
. 
. //////////////////////////
> //DIAGNOSTICS
. 
. ///FIRST PREDICT VOTING
> eststo clear
{txt}
{com}. 
. eststo: reg voted_k sex married age age2 parent children high_educ i.cohortbycountry, robust

{txt}Linear regression                               Number of obs     = {res}    61,155
                                                {txt}F(76, 61078)      =  {res}    79.51
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0790
                                                {txt}Root MSE          =    {res} .47994

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
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{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002239{col 26}{space 2} 9.15e-06{col 37}{space 1}  -24.47{col 46}{space 3}0.000{col 54}{space 4}-.0002419{col 67}{space 3} -.000206
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{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 9}54  {c |}{col 14}{res}{space 2}  .362913{col 26}{space 2} .1318138{col 37}{space 1}    2.75{col 46}{space 3}0.006{col 54}{space 4} .1045576{col 67}{space 3} .6212685
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .3749378{col 26}{space 2} .1320104{col 37}{space 1}    2.84{col 46}{space 3}0.005{col 54}{space 4} .1161971{col 67}{space 3} .6336785
{txt}{space 9}56  {c |}{col 14}{res}{space 2} .0895528{col 26}{space 2} .1337916{col 37}{space 1}    0.67{col 46}{space 3}0.503{col 54}{space 4}-.1726791{col 67}{space 3} .3517847
{txt}{space 9}57  {c |}{col 14}{res}{space 2} .0606271{col 26}{space 2} .1325938{col 37}{space 1}    0.46{col 46}{space 3}0.648{col 54}{space 4} -.199257{col 67}{space 3} .3205113
{txt}{space 9}58  {c |}{col 14}{res}{space 2} .0523438{col 26}{space 2} .1321966{col 37}{space 1}    0.40{col 46}{space 3}0.692{col 54}{space 4}-.2067619{col 67}{space 3} .3114494
{txt}{space 9}59  {c |}{col 14}{res}{space 2} .0179112{col 26}{space 2} .1316433{col 37}{space 1}    0.14{col 46}{space 3}0.892{col 54}{space 4}  -.24011{col 67}{space 3} .2759324
{txt}{space 9}60  {c |}{col 14}{res}{space 2} .0379863{col 26}{space 2} .1318969{col 37}{space 1}    0.29{col 46}{space 3}0.773{col 54}{space 4} -.220532{col 67}{space 3} .2965046
{txt}{space 9}61  {c |}{col 14}{res}{space 2} .3362851{col 26}{space 2}  .133135{col 37}{space 1}    2.53{col 46}{space 3}0.012{col 54}{space 4}   .07534{col 67}{space 3} .5972301
{txt}{space 9}62  {c |}{col 14}{res}{space 2} .2886379{col 26}{space 2} .1319035{col 37}{space 1}    2.19{col 46}{space 3}0.029{col 54}{space 4} .0301067{col 67}{space 3} .5471691
{txt}{space 9}63  {c |}{col 14}{res}{space 2} .2599009{col 26}{space 2} .1318871{col 37}{space 1}    1.97{col 46}{space 3}0.049{col 54}{space 4} .0014019{col 67}{space 3}    .5184
{txt}{space 9}64  {c |}{col 14}{res}{space 2} .2092209{col 26}{space 2} .1312019{col 37}{space 1}    1.59{col 46}{space 3}0.111{col 54}{space 4}-.0479352{col 67}{space 3} .4663771
{txt}{space 9}65  {c |}{col 14}{res}{space 2} .1304524{col 26}{space 2} .1311344{col 37}{space 1}    0.99{col 46}{space 3}0.320{col 54}{space 4}-.1265715{col 67}{space 3} .3874762
{txt}{space 9}66  {c |}{col 14}{res}{space 2} .4711777{col 26}{space 2} .1326924{col 37}{space 1}    3.55{col 46}{space 3}0.000{col 54}{space 4} .2111002{col 67}{space 3} .7312552
{txt}{space 9}67  {c |}{col 14}{res}{space 2} .3348746{col 26}{space 2} .1340505{col 37}{space 1}    2.50{col 46}{space 3}0.012{col 54}{space 4} .0721352{col 67}{space 3}  .597614
{txt}{space 9}68  {c |}{col 14}{res}{space 2} .4260406{col 26}{space 2} .1322107{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .1669073{col 67}{space 3} .6851739
{txt}{space 9}69  {c |}{col 14}{res}{space 2} .3489012{col 26}{space 2} .1321423{col 37}{space 1}    2.64{col 46}{space 3}0.008{col 54}{space 4} .0899019{col 67}{space 3} .6079005
{txt}{space 9}70  {c |}{col 14}{res}{space 2} .3127039{col 26}{space 2} .1320944{col 37}{space 1}    2.37{col 46}{space 3}0.018{col 54}{space 4} .0537985{col 67}{space 3} .5716092
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2498116{col 26}{space 2} .1310326{col 37}{space 1}   -1.91{col 46}{space 3}0.057{col 54}{space 4}-.5066357{col 67}{space 3} .0070126
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. predict z
{txt}(option {bf:xb} assumed; fitted values)
(2,218 missing values generated)

{com}. egen stdz = std(z)
{txt}(2218 missing values generated)

{com}. label variable stdz "std prediction"
{txt}
{com}. 
. ///NOW REGRESS SHARE OF COETHNICS ON PREDICTION
> eststo: areg coshare stdz, absorb(Forsamling) vce(cluster Kommun)

{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    61,155
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}     1,063
{txt}{col 49}F({res}   1{txt},{res}    285{txt}){col 67}= {res}      2.00
{txt}{col 49}Prob > F{col 67}= {res}    0.1585
{txt}{col 49}R-squared{col 67}= {res}    0.5436
{txt}{col 49}Adj R-squared{col 67}= {res}    0.5355
{txt}{col 49}Root MSE{col 67}= {res}    0.0091

{txt}{ralign 78:(Std. Err. adjusted for {res:286} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     coshare{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}stdz {c |}{col 14}{res}{space 2} -.000696{col 26}{space 2} .0004923{col 37}{space 1}   -1.41{col 46}{space 3}0.158{col 54}{space 4}-.0016649{col 67}{space 3} .0002729
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0095564{col 26}{space 2} 7.17e-14{col 37}{space 1} 1.3e+11{col 46}{space 3}0.000{col 54}{space 4} .0095564{col 67}{space 3} .0095564
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: areg coshare stdz if voted_coethnics_cum<0.25, absorb(Forsamling) vce(cluster Kommun)

{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,332
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       825
{txt}{col 49}F({res}   1{txt},{res}    277{txt}){col 67}= {res}      0.85
{txt}{col 49}Prob > F{col 67}= {res}    0.3566
{txt}{col 49}R-squared{col 67}= {res}    0.6772
{txt}{col 49}Adj R-squared{col 67}= {res}    0.6589
{txt}{col 49}Root MSE{col 67}= {res}    0.0060

{txt}{ralign 78:(Std. Err. adjusted for {res:278} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     coshare{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}stdz {c |}{col 14}{res}{space 2}-.0001376{col 26}{space 2} .0001491{col 37}{space 1}   -0.92{col 46}{space 3}0.357{col 54}{space 4}-.0004311{col 67}{space 3} .0001558
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0071925{col 26}{space 2} .0000235{col 37}{space 1}  305.74{col 46}{space 3}0.000{col 54}{space 4} .0071462{col 67}{space 3} .0072389
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. eststo: areg coshare stdz if voted_coethnics_cum>0.25 & voted_coethnics_cum<0.50, absorb(Forsamling) vce(cluster Kommun)

{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,261
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       754
{txt}{col 49}F({res}   1{txt},{res}    275{txt}){col 67}= {res}      6.07
{txt}{col 49}Prob > F{col 67}= {res}    0.0144
{txt}{col 49}R-squared{col 67}= {res}    0.7405
{txt}{col 49}Adj R-squared{col 67}= {res}    0.7270
{txt}{col 49}Root MSE{col 67}= {res}    0.0056

{txt}{ralign 78:(Std. Err. adjusted for {res:276} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     coshare{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}stdz {c |}{col 14}{res}{space 2}-.0003114{col 26}{space 2} .0001264{col 37}{space 1}   -2.46{col 46}{space 3}0.014{col 54}{space 4}-.0005602{col 67}{space 3}-.0000626
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0094421{col 26}{space 2} .0000107{col 37}{space 1}  881.05{col 46}{space 3}0.000{col 54}{space 4}  .009421{col 67}{space 3} .0094632
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. eststo: areg coshare stdz if voted_coethnics_cum>0.50 & voted_coethnics_cum<0.75, absorb(Forsamling) vce(cluster Kommun)

{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,261
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       413
{txt}{col 49}F({res}   1{txt},{res}    179{txt}){col 67}= {res}      0.71
{txt}{col 49}Prob > F{col 67}= {res}    0.3990
{txt}{col 49}R-squared{col 67}= {res}    0.6570
{txt}{col 49}Adj R-squared{col 67}= {res}    0.6474
{txt}{col 49}Root MSE{col 67}= {res}    0.0057

{txt}{ralign 78:(Std. Err. adjusted for {res:180} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     coshare{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}stdz {c |}{col 14}{res}{space 2} .0002149{col 26}{space 2} .0002542{col 37}{space 1}    0.85{col 46}{space 3}0.399{col 54}{space 4}-.0002867{col 67}{space 3} .0007164
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0086058{col 26}{space 2} .0000222{col 37}{space 1}  387.05{col 46}{space 3}0.000{col 54}{space 4} .0085619{col 67}{space 3} .0086497
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. eststo: areg coshare stdz if voted_coethnics_cum>0.75 & voted_coethnics_cum!=., absorb(Forsamling) vce(cluster Kommun)

{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    15,301
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       749
{txt}{col 49}F({res}   1{txt},{res}    253{txt}){col 67}= {res}      5.16
{txt}{col 49}Prob > F{col 67}= {res}    0.0239
{txt}{col 49}R-squared{col 67}= {res}    0.7020
{txt}{col 49}Adj R-squared{col 67}= {res}    0.6867
{txt}{col 49}Root MSE{col 67}= {res}    0.0108

{txt}{ralign 78:(Std. Err. adjusted for {res:254} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     coshare{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}stdz {c |}{col 14}{res}{space 2}-.0018685{col 26}{space 2} .0008223{col 37}{space 1}   -2.27{col 46}{space 3}0.024{col 54}{space 4}-.0034879{col 67}{space 3}-.0002492
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0127633{col 26}{space 2} .0001278{col 37}{space 1}   99.84{col 46}{space 3}0.000{col 54}{space 4} .0125115{col 67}{space 3} .0130151
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_diagnostics.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(sex age age2 married parent children high_educ stdz) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_diagnostics.tex"'})

{com}. 
. 
. 
. ///////////////////////////////
> ///Different quality measures
> eststo clear
{txt}
{com}. 
. eststo: areg voted_k stdcoshare stdsharevoted  $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    61,155
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}     1,063
{txt}{col 49}F({res}  78{txt},{res}    285{txt}){col 67}= {res}    139.51
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1097
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0928
{txt}{col 49}Root MSE{col 67}= {res}    0.4760

{txt}{ralign 78:(Std. Err. adjusted for {res:286} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0146054{col 26}{space 2} .0062406{col 37}{space 1}   -2.34{col 46}{space 3}0.020{col 54}{space 4} -.026889{col 67}{space 3}-.0023219
{txt}stdsharevo~d {c |}{col 14}{res}{space 2} .0278579{col 26}{space 2} .0043751{col 37}{space 1}    6.37{col 46}{space 3}0.000{col 54}{space 4} .0192464{col 67}{space 3} .0364695
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0184184{col 26}{space 2} .0046488{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4}  .009268{col 67}{space 3} .0275687
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0196021{col 26}{space 2} .0012871{col 37}{space 1}   15.23{col 46}{space 3}0.000{col 54}{space 4} .0170686{col 67}{space 3} .0221355
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002309{col 26}{space 2} .0000149{col 37}{space 1}  -15.45{col 46}{space 3}0.000{col 54}{space 4}-.0002603{col 67}{space 3}-.0002015
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0126731{col 26}{space 2} .0051668{col 37}{space 1}    2.45{col 46}{space 3}0.015{col 54}{space 4} .0025033{col 67}{space 3}  .022843
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0512624{col 26}{space 2} .0085266{col 37}{space 1}    6.01{col 46}{space 3}0.000{col 54}{space 4} .0344793{col 67}{space 3} .0680455
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0156248{col 26}{space 2} .0035499{col 37}{space 1}    4.40{col 46}{space 3}0.000{col 54}{space 4} .0086375{col 67}{space 3} .0226121
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0786349{col 26}{space 2} .0056352{col 37}{space 1}   13.95{col 46}{space 3}0.000{col 54}{space 4} .0675431{col 67}{space 3} .0897268
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .2623436{col 26}{space 2} .3462116{col 37}{space 1}    0.76{col 46}{space 3}0.449{col 54}{space 4}-.4191125{col 67}{space 3} .9437997
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1220551{col 26}{space 2} .2052272{col 37}{space 1}    0.59{col 46}{space 3}0.552{col 54}{space 4}-.2818982{col 67}{space 3} .5260084
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1292999{col 26}{space 2} .1451246{col 37}{space 1}    0.89{col 46}{space 3}0.374{col 54}{space 4}-.1563521{col 67}{space 3} .4149519
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1902934{col 26}{space 2} .2390281{col 37}{space 1}    0.80{col 46}{space 3}0.427{col 54}{space 4} -.280191{col 67}{space 3} .6607777
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1383275{col 26}{space 2} .1519114{col 37}{space 1}    0.91{col 46}{space 3}0.363{col 54}{space 4}-.1606831{col 67}{space 3} .4373381
{txt}{space 10}7  {c |}{col 14}{res}{space 2} .1928507{col 26}{space 2} .1342012{col 37}{space 1}    1.44{col 46}{space 3}0.152{col 54}{space 4}-.0713005{col 67}{space 3} .4570019
{txt}{space 10}8  {c |}{col 14}{res}{space 2} .1255583{col 26}{space 2} .1440179{col 37}{space 1}    0.87{col 46}{space 3}0.384{col 54}{space 4}-.1579155{col 67}{space 3}  .409032
{txt}{space 10}9  {c |}{col 14}{res}{space 2} .0972231{col 26}{space 2} .1391748{col 37}{space 1}    0.70{col 46}{space 3}0.485{col 54}{space 4}-.1767178{col 67}{space 3} .3711641
{txt}{space 9}10  {c |}{col 14}{res}{space 2} .1179773{col 26}{space 2} .1497336{col 37}{space 1}    0.79{col 46}{space 3}0.431{col 54}{space 4}-.1767467{col 67}{space 3} .4127012
{txt}{space 9}11  {c |}{col 14}{res}{space 2} .4864988{col 26}{space 2} .1547188{col 37}{space 1}    3.14{col 46}{space 3}0.002{col 54}{space 4} .1819622{col 67}{space 3} .7910354
{txt}{space 9}12  {c |}{col 14}{res}{space 2} .4002906{col 26}{space 2} .1501583{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4} .1047305{col 67}{space 3} .6958506
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .3735361{col 26}{space 2} .1371894{col 37}{space 1}    2.72{col 46}{space 3}0.007{col 54}{space 4}  .103503{col 67}{space 3} .6435691
{txt}{space 9}14  {c |}{col 14}{res}{space 2} .3976807{col 26}{space 2} .1447816{col 37}{space 1}    2.75{col 46}{space 3}0.006{col 54}{space 4} .1127038{col 67}{space 3} .6826576
{txt}{space 9}15  {c |}{col 14}{res}{space 2} .3801125{col 26}{space 2} .1534726{col 37}{space 1}    2.48{col 46}{space 3}0.014{col 54}{space 4} .0780289{col 67}{space 3} .6821962
{txt}{space 9}16  {c |}{col 14}{res}{space 2} .4954928{col 26}{space 2} .1299484{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} .2397125{col 67}{space 3} .7512731
{txt}{space 9}17  {c |}{col 14}{res}{space 2} .4781489{col 26}{space 2} .1339535{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .2144851{col 67}{space 3} .7418127
{txt}{space 9}18  {c |}{col 14}{res}{space 2} .4551433{col 26}{space 2} .1333718{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .1926245{col 67}{space 3} .7176621
{txt}{space 9}19  {c |}{col 14}{res}{space 2} .3929808{col 26}{space 2} .1346693{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .1279083{col 67}{space 3} .6580534
{txt}{space 9}20  {c |}{col 14}{res}{space 2} .4299422{col 26}{space 2} .1496748{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .1353339{col 67}{space 3} .7245506
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .4060715{col 26}{space 2} .1268672{col 37}{space 1}    3.20{col 46}{space 3}0.002{col 54}{space 4} .1563559{col 67}{space 3} .6557872
{txt}{space 9}22  {c |}{col 14}{res}{space 2} .3746843{col 26}{space 2} .1315336{col 37}{space 1}    2.85{col 46}{space 3}0.005{col 54}{space 4} .1157837{col 67}{space 3} .6335848
{txt}{space 9}23  {c |}{col 14}{res}{space 2} .4370592{col 26}{space 2} .1378688{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4}  .165689{col 67}{space 3} .7084295
{txt}{space 9}24  {c |}{col 14}{res}{space 2}  .350794{col 26}{space 2} .1387133{col 37}{space 1}    2.53{col 46}{space 3}0.012{col 54}{space 4} .0777614{col 67}{space 3} .6238265
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .3551094{col 26}{space 2} .1367143{col 37}{space 1}    2.60{col 46}{space 3}0.010{col 54}{space 4} .0860116{col 67}{space 3} .6242072
{txt}{space 9}26  {c |}{col 14}{res}{space 2} .4081993{col 26}{space 2} .1333961{col 37}{space 1}    3.06{col 46}{space 3}0.002{col 54}{space 4} .1456328{col 67}{space 3} .6707658
{txt}{space 9}27  {c |}{col 14}{res}{space 2} .4228946{col 26}{space 2} .1330842{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4} .1609419{col 67}{space 3} .6848472
{txt}{space 9}28  {c |}{col 14}{res}{space 2} .3072949{col 26}{space 2}  .130181{col 37}{space 1}    2.36{col 46}{space 3}0.019{col 54}{space 4} .0510568{col 67}{space 3}  .563533
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2479721{col 26}{space 2} .1396437{col 37}{space 1}   -1.78{col 46}{space 3}0.077{col 54}{space 4}-.5228359{col 67}{space 3} .0268917
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. 
. ///socioeconomci measure
> 
. eststo: areg voted_k stdcoshare stdsharevoted stdedu $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    60,737
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       970
{txt}{col 49}F({res}  79{txt},{res}    284{txt}){col 67}= {res}    136.09
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1085
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0928
{txt}{col 49}Root MSE{col 67}= {res}    0.4760

{txt}{ralign 78:(Std. Err. adjusted for {res:285} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0157496{col 26}{space 2} .0062323{col 37}{space 1}   -2.53{col 46}{space 3}0.012{col 54}{space 4}-.0280171{col 67}{space 3}-.0034822
{txt}stdsharevo~d {c |}{col 14}{res}{space 2} .0283105{col 26}{space 2} .0043885{col 37}{space 1}    6.45{col 46}{space 3}0.000{col 54}{space 4} .0196723{col 67}{space 3} .0369487
{txt}{space 6}stdedu {c |}{col 14}{res}{space 2}-.0061518{col 26}{space 2} .0040908{col 37}{space 1}   -1.50{col 46}{space 3}0.134{col 54}{space 4}-.0142039{col 67}{space 3} .0019003
{txt}{space 9}sex {c |}{col 14}{res}{space 2} .0180194{col 26}{space 2} .0046128{col 37}{space 1}    3.91{col 46}{space 3}0.000{col 54}{space 4} .0089398{col 67}{space 3}  .027099
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0196299{col 26}{space 2} .0012971{col 37}{space 1}   15.13{col 46}{space 3}0.000{col 54}{space 4} .0170768{col 67}{space 3}  .022183
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002311{col 26}{space 2} .0000151{col 37}{space 1}  -15.35{col 46}{space 3}0.000{col 54}{space 4}-.0002607{col 67}{space 3}-.0002015
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0122942{col 26}{space 2}  .005152{col 37}{space 1}    2.39{col 46}{space 3}0.018{col 54}{space 4} .0021533{col 67}{space 3} .0224352
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0510944{col 26}{space 2} .0085257{col 37}{space 1}    5.99{col 46}{space 3}0.000{col 54}{space 4} .0343128{col 67}{space 3}  .067876
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0156031{col 26}{space 2} .0035865{col 37}{space 1}    4.35{col 46}{space 3}0.000{col 54}{space 4} .0085436{col 67}{space 3} .0226626
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{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2475699{col 26}{space 2} .1400753{col 37}{space 1}   -1.77{col 46}{space 3}0.078{col 54}{space 4}-.5232875{col 67}{space 3} .0281477
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. 
. eststo: areg voted_k stdcoshare stdsharevoted stdtreiman stdinc stdedu $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    47,435
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       771
{txt}{col 49}F({res}  81{txt},{res}    279{txt}){col 67}= {res}    134.08
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1071
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0908
{txt}{col 49}Root MSE{col 67}= {res}    0.4768

{txt}{ralign 78:(Std. Err. adjusted for {res:280} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}stdcoshare {c |}{col 14}{res}{space 2}-.0235518{col 26}{space 2} .0056786{col 37}{space 1}   -4.15{col 46}{space 3}0.000{col 54}{space 4}-.0347301{col 67}{space 3}-.0123735
{txt}stdsharevo~d {c |}{col 14}{res}{space 2} .0326385{col 26}{space 2} .0038992{col 37}{space 1}    8.37{col 46}{space 3}0.000{col 54}{space 4} .0249629{col 67}{space 3} .0403141
{txt}{space 2}stdtreiman {c |}{col 14}{res}{space 2} .0022775{col 26}{space 2} .0038629{col 37}{space 1}    0.59{col 46}{space 3}0.556{col 54}{space 4}-.0053266{col 67}{space 3} .0098815
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{txt}{space 6}stdedu {c |}{col 14}{res}{space 2}-.0024042{col 26}{space 2} .0050279{col 37}{space 1}   -0.48{col 46}{space 3}0.633{col 54}{space 4}-.0123016{col 67}{space 3} .0074932
{txt}{space 9}sex {c |}{col 14}{res}{space 2}  .018972{col 26}{space 2} .0053859{col 37}{space 1}    3.52{col 46}{space 3}0.000{col 54}{space 4} .0083697{col 67}{space 3} .0295742
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0190356{col 26}{space 2}  .001423{col 37}{space 1}   13.38{col 46}{space 3}0.000{col 54}{space 4} .0162345{col 67}{space 3} .0218368
{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0002235{col 26}{space 2} .0000162{col 37}{space 1}  -13.76{col 46}{space 3}0.000{col 54}{space 4}-.0002554{col 67}{space 3}-.0001915
{txt}{space 5}married {c |}{col 14}{res}{space 2}   .00826{col 26}{space 2} .0054976{col 37}{space 1}    1.50{col 46}{space 3}0.134{col 54}{space 4} -.002562{col 67}{space 3} .0190821
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0473266{col 26}{space 2}  .010196{col 37}{space 1}    4.64{col 46}{space 3}0.000{col 54}{space 4} .0272558{col 67}{space 3} .0673973
{txt}{space 4}children {c |}{col 14}{res}{space 2} .0190605{col 26}{space 2} .0043142{col 37}{space 1}    4.42{col 46}{space 3}0.000{col 54}{space 4}  .010568{col 67}{space 3} .0275531
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0837494{col 26}{space 2} .0061775{col 37}{space 1}   13.56{col 46}{space 3}0.000{col 54}{space 4} .0715889{col 67}{space 3} .0959099
{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.3468779{col 26}{space 2} .1313756{col 37}{space 1}   -2.64{col 46}{space 3}0.009{col 54}{space 4}-.6054912{col 67}{space 3}-.0882647
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. 
. 
. 
. esttab using "C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_q.tex", ///
> se nofloat noconstant compress label nonumbers nogaps /// 
> keep(stdcoshare stdsharevoted stdtreiman stdinc stdedu) fragment nomtitles nonotes nolines replace 
{res}{txt}(output written to {browse  `"C:\Users\henrik\Dropbox\Ethnic Enclaves and Voting\Output\reg\reg_q.tex"'})

{com}. 
. eststo: areg voted_k stdedu  $cov i.cohortbycountry, absorb(Forsamling) vce(cluster Kommun)
{res}
{txt}Linear regression, absorbing indicators{col 49}Number of obs{col 67}= {res}    60,737
{txt}Absorbed variable: {bf:Forsamling}{col 49}No. of categories{col 67}= {res}       970
{txt}{col 49}F({res}  77{txt},{res}    284{txt}){col 67}= {res}    127.17
{txt}{col 49}Prob > F{col 67}= {res}    0.0000
{txt}{col 49}R-squared{col 67}= {res}    0.1077
{txt}{col 49}Adj R-squared{col 67}= {res}    0.0921
{txt}{col 49}Root MSE{col 67}= {res}    0.4762

{txt}{ralign 78:(Std. Err. adjusted for {res:285} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     voted_k{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
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{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2445716{col 26}{space 2} .1386152{col 37}{space 1}   -1.76{col 46}{space 3}0.079{col 54}{space 4}-.5174152{col 67}{space 3}  .028272
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. 
. 
. ///is the effect of the different estimates significantly different from each other 
> 
. ///the short run using long run sample 
> ///baseline results
> 
. *Merge on 2010 sample
. rename k k_first
{res}{txt}
{com}. merge 1:m LopNr using "D:\SCB_ConPol\Stata\VD\Valdelt_2010", keepusing (LopNr k)
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}       7,213,376
{txt}{col 9}from master{col 30}{res}          11,355{txt}  (_merge==1)
{col 9}from using{col 30}{res}       7,202,021{txt}  (_merge==2)

{col 5}matched{col 30}{res}          52,018{txt}  (_merge==3)
{col 5}{hline 41}

{com}. drop if _merge==2
{txt}(7,202,021 observations deleted)

{com}. 
. *generate variable to separate samples
. gen longsample = 1 if _merge==3
{txt}(11,355 missing values generated)

{com}. 
. 
. ///fit the two models
> eststo clear
{txt}
{com}. quietly reg voted_k stdcoshare $cov ib(last).cohortbycountry i.Forsamling if voted_coethnics_cum>0.75 
{txt}
{com}. estimates store b
{txt}
{com}. 
. quietly reg voted_k stdcoshare $cov ib(last).cohortbycountry i.Forsamling if voted_coethnics_cum>0.75 & longsample==1
{txt}
{com}. estimates store c
{txt}
{com}. 
. *Common covariance-matrix 
. suest b c, vce(cluster Kommun)
{res}
{txt}Simultaneous results for {stata estimates replay b:b}, {stata estimates replay c:c}

{col 49}Number of obs{col 67}= {res}    15,301

{txt}{ralign 78:(Std. Err. adjusted for {res:254} clusters in Kommun)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}b_mean       {txt}{c |}
{space 2}stdcoshare {c |}{col 14}{res}{space 2} .0219391{col 26}{space 2} .0049811{col 37}{space 1}    4.40{col 46}{space 3}0.000{col 54}{space 4} .0121763{col 67}{space 3}  .031702
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{txt}{space 8}age2 {c |}{col 14}{res}{space 2}-.0001806{col 26}{space 2} .0000184{col 37}{space 1}   -9.79{col 46}{space 3}0.000{col 54}{space 4}-.0002167{col 67}{space 3}-.0001444
{txt}{space 5}married {c |}{col 14}{res}{space 2} .0097552{col 26}{space 2} .0089531{col 37}{space 1}    1.09{col 46}{space 3}0.276{col 54}{space 4}-.0077927{col 67}{space 3}  .027303
{txt}{space 6}parent {c |}{col 14}{res}{space 2} .0852402{col 26}{space 2}  .018997{col 37}{space 1}    4.49{col 46}{space 3}0.000{col 54}{space 4} .0480067{col 67}{space 3} .1224737
{txt}{space 4}children {c |}{col 14}{res}{space 2}  .014175{col 26}{space 2} .0078468{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0012044{col 67}{space 3} .0295544
{txt}{space 3}high_educ {c |}{col 14}{res}{space 2} .0786217{col 26}{space 2} .0096016{col 37}{space 1}    8.19{col 46}{space 3}0.000{col 54}{space 4}  .059803{col 67}{space 3} .0974404
{txt}{space 12} {c |}
cohortbyco~y {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.4718141{col 26}{space 2} .0847818{col 37}{space 1}   -5.57{col 46}{space 3}0.000{col 54}{space 4}-.6379834{col 67}{space 3}-.3056448
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{space 2}Forsamling {c |}
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{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} .2930778{col 26}{space 2} .0587964{col 37}{space 1}    4.98{col 46}{space 3}0.000{col 54}{space 4}  .177839{col 67}{space 3} .4083166
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{res}b_lnvar      {txt}{c |}
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{res}c_mean       {txt}{c |}
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{txt}{space 12} {c |}
cohortbyco~y {c |}
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{txt}{space 12} {c |}
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{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}c_lnvar      {txt}{c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-1.511189{col 26}{space 2} .0127478{col 37}{space 1} -118.55{col 46}{space 3}0.000{col 54}{space 4}-1.536175{col 67}{space 3}-1.486204
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *test
. test [b_mean]stdcoshare-[c_mean]stdcoshare = 0

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[b_mean]stdcoshare - [c_mean]stdcoshare = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    2.73
{txt}{col 10}Prob > chi2 =  {res}  0.0983
{txt}
{com}. 
. *save to text
. file write stats "P-value long vs short sample: `r(p)' "        
{txt}
{com}. 
. *Add number of obs too
. sum LopNr

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}LopNr {c |}{res}     63,373     6788488     4611297        548   1.61e+07
{txt}
{com}. file write stats "Total obs: `r(N)' " 
{txt}
{com}. 
. file close stats
{txt}
{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Userdata\Shared\Logs\Ethnic Enclaves, political participation\log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 3 Jun 2020, 19:21:54
{txt}{.-}
{smcl}
{txt}{sf}{ul off}